{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 单层SNN识别MNIST\n",
    "\n",
    "作者: [赵振宇](https://github.com/15947470421)\n",
    "\n",
    "感谢朱文凯为本教程提供的建议"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本节教程将介绍如何使用编码器与替代梯度方法训练一个十分简单的MNIST分类网络。\n",
    "\n",
    "本节使用的训练代码也将作为一个通用模板，在接下来的课程中，我们代码的架构还会沿用本节的代码，只针对部分代码进行修改"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先我们引入需要使用的库\n",
    "\n",
    "`torchsummary`是一个用于预览网络规模以及参数量的库，可以在当前的anaconda环境的命令行环境中使用`pip install torchsummary`安装"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.data as data\n",
    "import torchvision\n",
    "import numpy as np\n",
    "from torchsummary import summary # 用于预览神经网络结构的库\n",
    "from spikingjelly.activation_based import neuron, encoding, functional, surrogate\n",
    "from spikingjelly import visualizing\n",
    "from matplotlib import pyplot as plt\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先我们进行一些基本配置，包括配置运行硬件平台，指定数据集地址等\n",
    "\n",
    "注意，这里需要我们手动建立对应的文件夹目录。`./`表示当前jupyter notebook文件所在的地址\n",
    "\n",
    "最终，路径中应该类似如下结构：\n",
    "+ dataset\n",
    "+ model_save\n",
    "\n",
    "  ++ auto_save\n",
    "\n",
    "  ++ history_save\n",
    "  \n",
    "+ 当前jupyter notebook代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "device: cuda:0\n"
     ]
    }
   ],
   "source": [
    "# 配置基本信息\n",
    "# 配置运行硬件平台\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"device: {device}\")\n",
    "# 指定数据集地址\n",
    "dataset_dir = \"./dataset\" # 数据集地址\n",
    "# 指定数据集的batch_size\n",
    "batch_size = 64\n",
    "# 设定自动保存模型的地址 \n",
    "# 请务必确保已经建立文件夹\n",
    "model_auto_save_dir = \"./model_save/auto_save/\"\n",
    "# 设定手动保存模型的地址 \n",
    "# 请务必确保已经建立文件夹\n",
    "model_history_save_dir = \"./model_save/history_save/\"\n",
    "# 设定外部图片地址\n",
    "# 请务必确保对应文件夹中有该图片，若无外部图片请注释掉该行代码\n",
    "# img_path = \"./dataset/images1.jpg\"\n",
    "# jupyter输出不打印省略号，网络参数过多时不建议开启\n",
    "# torch.set_printoptions(threshold=np.inf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "完成基本的配置后，我们来导入MNIST数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集大小：60000\n",
      "测试集大小：10000\n"
     ]
    }
   ],
   "source": [
    "# 导入数据集\n",
    "\n",
    "# 加载训练数据集\n",
    "train_dataset = torchvision.datasets.MNIST(\n",
    "    root=dataset_dir,\n",
    "    train=True,\n",
    "    transform=torchvision.transforms.ToTensor(),\n",
    "    download=True\n",
    ")\n",
    "# 加载测试数据集\n",
    "test_dataset = torchvision.datasets.MNIST(\n",
    "    root=dataset_dir,\n",
    "    train=False,\n",
    "    transform=torchvision.transforms.ToTensor(),\n",
    "    download=True\n",
    ")\n",
    "# 打印训练数据集长度\n",
    "train_dataset_size = len(train_dataset)\n",
    "print(f\"训练集大小：{train_dataset_size}\")\n",
    "# 打印测试数据集长度\n",
    "test_dataset_size = len(test_dataset)\n",
    "print(f\"测试集大小：{test_dataset_size}\")\n",
    "\n",
    "# 使用DataLoader加载训练数据\n",
    "train_dataloader = data.DataLoader(\n",
    "    dataset=train_dataset,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=True,\n",
    "    drop_last=True,\n",
    "    pin_memory=True\n",
    ")\n",
    "# 使用DataLoader加载测试数据\n",
    "test_dataloader = data.DataLoader(\n",
    "    dataset=test_dataset,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=False,\n",
    "    drop_last=False,\n",
    "    pin_memory=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后，我们定义我们的脉冲神经网络架构，最后我们通过`summary`函数来查看网络架构\n",
    "\n",
    "网络架构很简单，首先将28 * 28的MNIST数据通过`flatten`层展开，然后通过一个全连接`linear_1`层实现权重累加，最后将结果输入到10输出的LIF神经元`lif_layer_1`层\n",
    "+ flatten()\n",
    "+ linear(in_features=28 * 28, out_features=10, bias=False)\n",
    "+ lif(tau=2.0, surrogate_function=surrogate.ATan())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "神经网络信息\n",
      "SNN(\n",
      "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
      "  (linear_1): Linear(in_features=784, out_features=10, bias=False)\n",
      "  (lif_layer_1): LIFNode(\n",
      "    v_threshold=1.0, v_reset=0.0, detach_reset=False, step_mode=s, backend=torch, tau=2.0\n",
      "    (surrogate_function): ATan(alpha=2.0, spiking=True)\n",
      "  )\n",
      ")\n",
      "输入数据格式 torch.Size([1, 1, 28, 28])\n",
      "输出数据格式 torch.Size([1, 10])\n",
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "           Flatten-1                  [-1, 784]               0\n",
      "            Linear-2                   [-1, 10]           7,840\n",
      "              ATan-3                   [-1, 10]               0\n",
      "           LIFNode-4                   [-1, 10]               0\n",
      "================================================================\n",
      "Total params: 7,840\n",
      "Trainable params: 7,840\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 0.01\n",
      "Params size (MB): 0.03\n",
      "Estimated Total Size (MB): 0.04\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# 定义网络架构\n",
    "class SNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(SNN, self).__init__()\n",
    "        self.flatten = nn.Flatten()\n",
    "        self.linear_1 = nn.Linear(in_features=28 * 28, out_features=10, bias=False)\n",
    "        self.lif_layer_1 = neuron.LIFNode(tau=2.0, surrogate_function=surrogate.ATan())\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.flatten(x)\n",
    "        x = self.linear_1(x)\n",
    "        x = self.lif_layer_1(x)\n",
    "        # x = F.softmax(x, dim=0) # softmax激活层用于多元分类\n",
    "        return x\n",
    "    \n",
    "# 实例化神经网络\n",
    "net = SNN()\n",
    "net = net.to(device) # 将神经网络转换\n",
    "\n",
    "# 打印神经网络信息\n",
    "print(\"神经网络信息\")\n",
    "print(net)\n",
    "\n",
    "# 测试神经网络的输出格式\n",
    "# 设置满足要求的输入\n",
    "input = torch.ones([1, 1, 28, 28])\n",
    "input = input.to(device)\n",
    "print(\"输入数据格式\", input.shape)\n",
    "\n",
    "output = net(input)\n",
    "print(\"输出数据格式\", output.shape)\n",
    "\n",
    "# 查看神经网络模型大小\n",
    "summary(net, input_size=[[1, 28, 28]])\n",
    "\n",
    "# 优化一次参数后，需要重置网络的状态，因为SNN的神经元是有“记忆”的\n",
    "functional.reset_net(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "定义训练神经网络的超参数，这里我们使用Adam作为优化器，设置数据编码器为泊松编码器`encoding.PoissonEncoder()`，设置仿真周期T为20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置神经网络超参数\n",
    "# 设置损失函数\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "loss_fn = loss_fn.to(device) # 将损失函数转换\n",
    "\n",
    "# 设置优化器\n",
    "learning_rate = 0.001 # 设置学习率\n",
    "lambda_val = 0.0 # 设置正则化系数Lambda\n",
    "optim = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=lambda_val)\n",
    "\n",
    "# 定义输入数据编码器\n",
    "encoder = encoding.PoissonEncoder()\n",
    "\n",
    "# 设定仿真时长T\n",
    "T = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "设定训练轮数`epoch`以及定义训练过程中所需的临时参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设定训练轮数\n",
    "epoch = 5\n",
    "# 定义训练集loss以及准确率变量\n",
    "loss_train = 0.0 # 训练损失值\n",
    "loss_train_sum = 0.0 # 训练损失值累加值\n",
    "loss_train_mean = 0.0 # 这一轮训练损失值的平均值\n",
    "loss_train_store = np.zeros(epoch) # 训练损失值存储数组\n",
    "accuracy_train = 0 # 训练准确数\n",
    "accuracy_rate_train = 0.0 # 训练准确率\n",
    "accuracy_rate_train_max = 0.0 # 最大训练准确率\n",
    "accuracy_rate_train_store = np.zeros(epoch) # 训练准确率存储素数组\n",
    "# 定义交叉验证集loss以及准确率变量\n",
    "loss_cv = 0.0 # 验证损失值\n",
    "loss_cv_sum = 0.0 # 验证损失值累加值\n",
    "loss_cv_mean = 0.0 # 这一轮验证损失值的平均值\n",
    "loss_cv_store = np.zeros(epoch) # 验证损失值存储数组\n",
    "accuracy_cv = 0 # 验证准确数\n",
    "accuracy_rate_cv = 0.0 # 验证准确率\n",
    "accuracy_rate_cv_max = 0.0 # 最大验证准确率\n",
    "accuracy_rate_cv_store = np.zeros(epoch) # 验证准确率存储素数组"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练网络的主循环代码\n",
    "\n",
    "这里，我们将标签转化为独热编码，在循环了仿真周期T后，我们需要统计输出层的10个神经元的脉冲发放率，取发放率最高的表示推理结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------第0轮训练开始------------\n",
      "loss train:  0.0276, accuracy rate train:  86.3050%, loss cv:  0.0196, accuracy rate cv:  91.1800%\n",
      "-----------第1轮训练开始------------\n",
      "loss train:  0.0190, accuracy rate train:  90.7500%, loss cv:  0.0178, accuracy rate cv:  91.5300%\n",
      "-----------第2轮训练开始------------\n",
      "loss train:  0.0177, accuracy rate train:  91.3367%, loss cv:  0.0169, accuracy rate cv:  91.8400%\n",
      "-----------第3轮训练开始------------\n",
      "loss train:  0.0170, accuracy rate train:  91.6000%, loss cv:  0.0167, accuracy rate cv:  91.5900%\n",
      "-----------第4轮训练开始------------\n",
      "loss train:  0.0167, accuracy rate train:  91.7750%, loss cv:  0.0164, accuracy rate cv:  92.0200%\n",
      "-------------------------------训练完成-------------------------------\n"
     ]
    }
   ],
   "source": [
    "# 训练网络\n",
    "\n",
    "#########################开始训练########################\n",
    "\n",
    "for i in range(epoch):\n",
    "    print(\"-----------第%d轮训练开始------------\" %i)\n",
    "    # 每轮loss累加值清零\n",
    "    loss_train_sum = 0\n",
    "    loss_cv_sum = 0\n",
    "    # 开始训练神经网络\n",
    "    net.train()\n",
    "    # 这一轮的训练计数\n",
    "    train_step = 0\n",
    "    # 准确性变量清零\n",
    "    accuracy_train = 0\n",
    "    for data in train_dataloader:\n",
    "        inputs, labels = data\n",
    "        inputs = inputs.to(device)\n",
    "        labels = labels.to(device)\n",
    "        label_onehot = F.one_hot(labels, 10).float() # 将标签转化为独热编码\n",
    "\n",
    "        out_fr = 0. # 输出神经元脉冲发放率\n",
    "        # out_fr是shape=[batch_size, 10]的tensor\n",
    "        # 记录整个仿真时长内，输出层的10个神经元的脉冲发放率\n",
    "        for t in range(T):\n",
    "            encoded_img = encoder(inputs) # 先将输入图像编码为脉冲形式数据\n",
    "            out_fr += net(encoded_img) # 将输出层的10个神经元的脉冲输出累加\n",
    "        out_fr = out_fr / T # 除以时间，得到输出层的10个神经元的脉冲发放率\n",
    "        # 计算损失函数\n",
    "        loss_train = F.mse_loss(out_fr, label_onehot)\n",
    "        # 损失函数为输出层神经元的脉冲发放频率，与真实类别的MSE\n",
    "        # 这样的损失函数会使得：当标签i给定时，输出层中第i个神经元的脉冲发放频率趋近1，而其他神经元的脉冲发放频率趋近0\n",
    "        # 累加损失函数\n",
    "        loss_train_sum += loss_train.item()\n",
    "        # 将分类概率转化为对应标签\n",
    "        pred_label = out_fr.argmax(1)\n",
    "        # 累加计算结果准确性\n",
    "        accuracy_train += (pred_label == labels).sum()\n",
    "        # 清零优化器的累计梯度\n",
    "        optim.zero_grad()\n",
    "        # 计算梯度\n",
    "        loss_train.backward()\n",
    "        # 优化器开始优化\n",
    "        optim.step()\n",
    "        \n",
    "        # 优化一次参数后，需要重置网络的状态，因为SNN的神经元是有“记忆”的\n",
    "        functional.reset_net(net)\n",
    "\n",
    "    # 计算平均训练集损失函数loss_train\n",
    "    loss_train_mean = loss_train_sum / (train_dataset_size/batch_size)\n",
    "    loss_train_store[i] = loss_train_mean # 将loss记录，方便后续输出loss曲线\n",
    "    # 计算训练集准确率\n",
    "    accuracy_rate_train = (accuracy_train/train_dataset_size) * 100\n",
    "    accuracy_rate_train_store[i] = accuracy_rate_train\n",
    "\n",
    "\n",
    "    # 开始测试神经网络\n",
    "    net.eval()\n",
    "    # 这一轮的测试计数\n",
    "    test_step = 0\n",
    "    # 准确性变量清零\n",
    "    accuracy_cv = 0\n",
    "    with torch.no_grad():\n",
    "        for data in test_dataloader:\n",
    "            inputs, labels = data\n",
    "            inputs = inputs.to(device)\n",
    "            labels = labels.to(device)\n",
    "            label_onehot = F.one_hot(labels, 10).float()\n",
    "            out_fr = 0.0\n",
    "            for t in range(T):\n",
    "                encoded_img = encoder(inputs)\n",
    "                # 输入神经网络并运行\n",
    "                out_fr += net(encoded_img)\n",
    "            out_fr = out_fr / T\n",
    "            # 计算损失函数\n",
    "            loss_cv = F.mse_loss(out_fr, label_onehot)\n",
    "            # 累加损失函数\n",
    "            loss_cv_sum += loss_cv.item()\n",
    "            # 将分类概率转化为对应标签\n",
    "            pred_label = out_fr.argmax(1)\n",
    "            # 累加计算结果准确性\n",
    "            accuracy_cv += (pred_label == labels).sum()\n",
    "            test_step += 1\n",
    "            # 优化一次参数后，需要重置网络的状态，因为SNN的神经元是有“记忆”的\n",
    "            functional.reset_net(net)\n",
    "\n",
    "        # 计算交叉验证集损失函数loss cv\n",
    "        loss_cv_mean = loss_cv_sum / (test_dataset_size/batch_size)\n",
    "        loss_cv_store[i] = loss_cv_mean # 将loss记录，方便后续输出loss曲线\n",
    "        # 计算交叉验证集准确率\n",
    "        accuracy_rate_cv = (accuracy_cv/test_dataset_size) * 100\n",
    "        accuracy_rate_cv_store[i] = accuracy_rate_cv\n",
    "\n",
    "    # 输出该轮训练结果\n",
    "    print(f\"loss train: {loss_train_mean: .4f}, accuracy rate train: {accuracy_rate_train.item(): .4f}%, loss cv: {loss_cv_mean: .4f}, accuracy rate cv: {accuracy_rate_cv.item(): .4f}%\")\n",
    "    # 自动保存训练过程中准确率大于之前的神经网络模型\n",
    "    if (accuracy_rate_train + accuracy_rate_cv > accuracy_rate_train_max + accuracy_rate_cv_max):\n",
    "        torch.save(net, model_auto_save_dir + \"model_auto_save_acc%d.pth\" %accuracy_rate_cv)\n",
    "        accuracy_rate_train_max = accuracy_rate_train # 记录最大训练准确率\n",
    "        accuracy_rate_cv_max = accuracy_rate_cv # 记录最大训练准确率\n",
    "    \n",
    "#########################结束训练########################\n",
    "print(\"-------------------------------训练完成-------------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------损失函数Loss随训练轮数epoch的变化图像---------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------准确率accuracy随训练轮数epoch的变化图像---------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出loss曲线\n",
    "print(\"---------损失函数Loss随训练轮数epoch的变化图像---------\")\n",
    "plt.plot(np.arange(epoch), loss_train_store, np.arange(epoch), loss_cv_store)\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend([\"loss train\", \"loss cv\"])\n",
    "plt.title(\"loss curve\")\n",
    "plt.show()\n",
    "\n",
    "# 画出accuracy曲线\n",
    "print(\"---------准确率accuracy随训练轮数epoch的变化图像---------\")\n",
    "plt.plot(np.arange(epoch), accuracy_rate_train_store, np.arange(epoch), accuracy_rate_cv_store)\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"accuracy rate\")\n",
    "plt.legend([\"accuracy rate train\", \"accuracy rate cv\"])\n",
    "plt.title(\"accuracy rate curve\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面，让我们单独运行推理过程，每100次输出结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "输出结果 tensor([7, 2, 1, 0, 4, 1, 4, 9, 6, 9, 0, 6, 9, 0, 1, 5, 9, 7, 0, 4, 9, 6, 6, 5,\n",
      "        4, 0, 7, 4, 0, 1, 3, 1, 3, 4, 7, 2, 7, 1, 3, 1, 1, 7, 4, 2, 3, 5, 1, 2,\n",
      "        4, 4, 6, 3, 5, 5, 6, 0, 4, 1, 9, 5, 7, 8, 9, 2], device='cuda:0')\n",
      "原始标签 tensor([7, 2, 1, 0, 4, 1, 4, 9, 5, 9, 0, 6, 9, 0, 1, 5, 9, 7, 3, 4, 9, 6, 6, 5,\n",
      "        4, 0, 7, 4, 0, 1, 3, 1, 3, 4, 7, 2, 7, 1, 2, 1, 1, 7, 4, 2, 3, 5, 1, 2,\n",
      "        4, 4, 6, 3, 5, 5, 6, 0, 4, 1, 9, 5, 7, 8, 9, 3], device='cuda:0')\n",
      "输出结果 tensor([0, 9, 6, 8, 8, 5, 6, 1, 1, 9, 8, 9, 2, 3, 5, 5, 9, 4, 2, 1, 9, 2, 9, 2,\n",
      "        0, 2, 6, 4, 0, 0, 1, 2, 8, 4, 4, 8, 9, 0, 1, 2, 3, 7, 8, 9, 0, 1, 2, 3,\n",
      "        4, 7, 8, 9, 7, 3, 0, 3, 1, 8, 7, 6, 4, 0, 2, 6], device='cuda:0')\n",
      "原始标签 tensor([0, 9, 6, 8, 8, 5, 6, 1, 1, 9, 8, 9, 2, 3, 5, 5, 9, 4, 2, 1, 9, 3, 9, 2,\n",
      "        0, 6, 0, 4, 0, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 2, 3, 7, 8, 9, 0, 1, 2, 3,\n",
      "        4, 7, 8, 9, 7, 3, 0, 3, 1, 8, 7, 6, 4, 0, 2, 6], device='cuda:0')\n",
      "模型预测正确数 9181 模型测试总数 10000\n",
      "模型预测准确率: 91.81%\n"
     ]
    }
   ],
   "source": [
    "# 加载某一轮训练的模型进行预测\n",
    "# net = torch.load(\"model_save/auto_save/model_auto_save_acc95.pth\")\n",
    "# net = torch.load(\"module_save/history_save/module_save_acc63.pth\")\n",
    "\n",
    "# 开始使用神经网络预测\n",
    "net.eval()\n",
    "# 总测试次数\n",
    "test_step = 0\n",
    "# 统计准确性\n",
    "accuracy = 0\n",
    "with torch.no_grad(): # 预测阶段不进行梯度下降\n",
    "    for data in test_dataloader:\n",
    "        inputs, labels = data\n",
    "        inputs = inputs.to(device) # 将数据转换\n",
    "        labels = labels.to(device) # 将数据转换\n",
    "        label_onehot = F.one_hot(labels, 10).float()\n",
    "        out_fr = 0.0\n",
    "        for t in range(T):\n",
    "            encoded_img = encoder(inputs)\n",
    "            out_fr += net(encoded_img)\n",
    "        out_fr = out_fr / T\n",
    "        # 将分类概率转化为对应标签\n",
    "        pred_label = out_fr.argmax(1)\n",
    "        # 每100次输出结果\n",
    "        if (test_step%100==0):\n",
    "            print(\"输出结果\", pred_label)\n",
    "            print(\"原始标签\", labels)\n",
    "        # 累加计算结果准确性\n",
    "        accuracy += (pred_label == labels).sum()\n",
    "        test_step += 1\n",
    "\n",
    "        # 优化一次参数后，需要重置网络的状态，因为SNN的神经元是有“记忆”的\n",
    "        functional.reset_net(net)\n",
    "\n",
    "accuracy_rate = accuracy/test_dataset_size\n",
    "print(\"模型预测正确数\", accuracy.item(), \"模型测试总数\", test_dataset_size)\n",
    "print(\"模型预测准确率: %.2f%%\" %(accuracy_rate.item()*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了更直观的感受，我们可以选择取出单张图片进行预测，通过更改`index`可以选择不同的输入图像\n",
    "\n",
    "想要在此时获取输出层神经元的膜电位，是有一些难度的，需要我们使用“钩子”hook功能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始图像\n",
      "label: 0\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 取出单张图片进行预测\n",
    "\n",
    "# 载入原始数据\n",
    "# 加载测试集中的单张图片\n",
    "# 保存绘图用数据\n",
    "net.eval()\n",
    "# 注册钩子\n",
    "output_layer = net.lif_layer_1 # 输出层\n",
    "output_layer.v_seq = []\n",
    "output_layer.s_seq = []\n",
    "def save_hook(m, x, y):\n",
    "    m.v_seq.append(m.v.unsqueeze(0))\n",
    "    m.s_seq.append(y.unsqueeze(0))\n",
    "\n",
    "hook = output_layer.register_forward_hook(save_hook)\n",
    "\n",
    "# 加载测试集中的单张图片\n",
    "index = 8373\n",
    "input, label = test_dataset[index]\n",
    "input = torchvision.transforms.ToPILImage()(input)\n",
    "plt.imshow(input)\n",
    "print(\"原始图像\")\n",
    "print(f\"label: {label}\")\n",
    "plt.show()\n",
    "\n",
    "# 开始使用神经网络预测\n",
    "net.eval()\n",
    "with torch.no_grad():\n",
    "    img, label = test_dataset[index]\n",
    "    img = img.to(device)\n",
    "    out_fr = 0.\n",
    "    for t in range(T):\n",
    "        encoded_img = encoder(img)\n",
    "        out_fr += net(encoded_img)\n",
    "    out_spikes_counter_frequency = (out_fr / T).cpu().numpy()\n",
    "\n",
    "    output_layer.v_seq = torch.cat(output_layer.v_seq)\n",
    "    output_layer.s_seq = torch.cat(output_layer.s_seq)\n",
    "    v_t_array = output_layer.v_seq.cpu().numpy().squeeze()  # v_t_array[i][j]表示神经元i在j时刻的电压值\n",
    "    s_t_array = output_layer.s_seq.cpu().numpy().squeeze()  # s_t_array[i][j]表示神经元i在j时刻释放的脉冲，为0或1\n",
    "\n",
    "    # 膜电位热力图和输出脉冲结果\n",
    "    figsize = (12, 8)\n",
    "    dpi = 100\n",
    "    visualizing.plot_2d_heatmap(array=v_t_array, title='membrane potentials', xlabel='simulating step',\n",
    "                                ylabel='neuron index', int_x_ticks=True, x_max=T, figsize=figsize, dpi=dpi)\n",
    "\n",
    "\n",
    "    visualizing.plot_1d_spikes(spikes=s_t_array, title='membrane sotentials', xlabel='simulating step',\n",
    "                            ylabel='neuron index', figsize=figsize, dpi=dpi)\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "hook.remove()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存神经网络模型\n",
    "# torch.save(net, model_history_save_dir + \"module_save_acc.pth\")\n",
    "# print(\"模型已保存\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SNN(\n",
      "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
      "  (linear_1): Linear(in_features=784, out_features=10, bias=False)\n",
      "  (lif_layer_1): LIFNode(\n",
      "    v_threshold=1.0, v_reset=0.0, detach_reset=False, step_mode=s, backend=torch, tau=2.0\n",
      "    (surrogate_function): ATan(alpha=2.0, spiking=True)\n",
      "  )\n",
      ")\n",
      "SNN.linear_1.weight Parameter containing:\n",
      "tensor([[ 9.6435e-03, -9.3254e-03, -1.4973e-03, -4.6127e-03, -3.2445e-03,\n",
      "          2.3353e-02,  3.4768e-02, -3.1842e-02, -1.1396e-02, -2.7142e-02,\n",
      "         -3.3329e-03,  2.6733e-02,  1.4400e-02,  2.5748e-02, -3.4509e-02,\n",
      "          1.5211e-02, -1.0122e-02,  8.1712e-03, -1.7914e-02, -6.2396e-03,\n",
      "          1.0336e-03,  1.1991e-02,  9.6881e-04, -1.8907e-02, -1.8555e-02,\n",
      "          1.0539e-03,  3.6112e-03, -9.0087e-03,  6.2795e-04,  1.1167e-02,\n",
      "          1.0810e-02,  1.3528e-02,  1.9179e-02, -1.4478e-02, -2.6765e-02,\n",
      "          1.4206e-02,  3.1271e-02,  1.0957e-03, -5.9162e-02,  1.1181e-01,\n",
      "          1.3387e-01,  9.7106e-02, -1.5293e-02,  2.4963e-04,  4.3792e-02,\n",
      "          4.8547e-02,  2.7675e-02,  2.9697e-02,  4.2847e-03, -4.8364e-02,\n",
      "          1.6085e-02, -9.2912e-03,  5.0279e-03,  1.1644e-02,  3.0115e-02,\n",
      "          1.7028e-02, -2.5407e-02,  1.5510e-02,  3.5014e-02,  4.2382e-02,\n",
      "          4.7817e-02, -4.8970e-02, -1.1783e-01, -7.8218e-02, -2.0289e-01,\n",
      "         -2.1361e-01, -2.1886e-01, -1.5740e-01, -1.4944e-01, -2.4482e-01,\n",
      "         -2.6790e-01, -3.6012e-01, -1.9452e-01, -3.4387e-01, -3.0585e-01,\n",
      "         -2.2687e-01, -2.7214e-01, -1.9221e-01, -9.8049e-02, -7.5134e-03,\n",
      "         -1.3570e-02, -2.5225e-03,  1.2686e-02,  1.3688e-02, -7.8109e-03,\n",
      "         -2.3949e-02, -3.0825e-02,  5.3878e-03, -1.8312e-02, -1.2668e-01,\n",
      "         -2.3102e-01, -1.7788e-01, -6.8237e-02, -2.3913e-01, -2.1101e-01,\n",
      "         -1.0924e-01, -9.1512e-02, -3.5647e-02, -1.0645e-01, -1.0750e-01,\n",
      "         -8.5766e-02, -1.0414e-01, -1.5975e-01, -1.4381e-01, -1.7291e-01,\n",
      "         -1.8429e-01, -1.9459e-01, -9.3703e-02,  2.2071e-02, -3.6819e-02,\n",
      "         -9.5751e-02,  1.0653e-02,  7.9704e-03, -2.4958e-02, -2.7355e-02,\n",
      "         -1.3773e-01, -1.0154e-01, -2.3980e-01, -1.9210e-01, -8.7454e-02,\n",
      "         -3.5494e-02, -2.2650e-02, -3.0549e-02, -4.8639e-02, -4.4185e-02,\n",
      "          6.6090e-03, -2.6528e-02,  2.2912e-02, -7.8762e-02,  1.4622e-02,\n",
      "         -4.0279e-02, -8.1876e-03, -2.6759e-02, -7.2044e-02, -4.6343e-03,\n",
      "         -3.0261e-02, -1.4759e-01, -2.0962e-01, -9.9193e-02,  1.3368e-02,\n",
      "          4.2686e-04,  2.0180e-02, -8.2905e-02, -5.4004e-02, -1.0375e-01,\n",
      "         -2.3778e-01, -2.7404e-02, -6.6937e-02, -5.0726e-02, -4.6919e-02,\n",
      "          8.7846e-03, -1.9030e-02,  9.6606e-03,  5.4547e-02,  8.7905e-02,\n",
      "          8.3575e-02,  4.0851e-02,  3.3950e-02,  4.5358e-02,  8.2229e-03,\n",
      "         -3.6624e-02, -3.7612e-02, -5.1481e-03, -3.9286e-02, -1.8522e-01,\n",
      "         -3.2414e-01, -2.1699e-01, -1.4135e-01,  1.7139e-02, -2.6744e-03,\n",
      "         -6.8732e-02, -6.7006e-02, -1.2928e-01, -2.0736e-02, -8.5052e-02,\n",
      "         -3.1875e-02, -1.0178e-01, -1.3855e-01, -2.2626e-02, -5.3381e-02,\n",
      "         -1.2157e-02,  1.6405e-02,  3.6454e-02,  5.5017e-02,  2.9931e-02,\n",
      "          6.0568e-02,  5.9655e-02,  2.5454e-02,  1.4493e-03,  8.4207e-02,\n",
      "          7.5169e-02, -6.1532e-02, -2.1245e-01, -4.5593e-01, -2.4640e-01,\n",
      "         -1.6748e-01, -1.0011e-02,  3.0824e-02, -3.7518e-02, -5.3941e-02,\n",
      "         -1.8810e-01, -1.1075e-03,  3.0728e-02, -2.8916e-02, -7.0713e-02,\n",
      "         -1.9199e-02, -1.1604e-02,  4.8236e-03, -1.2748e-02,  5.7356e-02,\n",
      "          1.0820e-02,  1.0484e-02,  4.1641e-02,  1.1727e-01,  9.7896e-02,\n",
      "          9.5636e-02,  7.7841e-03,  2.3476e-02,  2.7035e-02, -3.7481e-02,\n",
      "         -2.3932e-01, -3.3523e-01, -3.2753e-01, -7.5504e-02,  1.1016e-01,\n",
      "         -3.3863e-02,  2.1458e-03,  3.2192e-02, -2.2682e-01, -7.7323e-02,\n",
      "          7.3824e-03, -3.7989e-02, -1.5043e-02,  6.2039e-03,  3.1018e-02,\n",
      "         -3.1037e-02,  4.4565e-02, -1.5321e-02,  4.0035e-02,  9.8211e-02,\n",
      "          1.6032e-01,  1.3396e-01,  6.6436e-02,  6.3846e-02,  4.0853e-02,\n",
      "          1.4218e-02,  2.8577e-02,  5.1995e-02, -2.1127e-01, -4.5192e-01,\n",
      "         -3.0026e-01, -5.5379e-02, -1.8197e-02, -1.2014e-02,  1.6651e-02,\n",
      "         -5.5529e-02, -1.2863e-01,  2.3837e-02, -4.3598e-02,  4.5914e-03,\n",
      "         -4.5726e-02,  1.5482e-02,  3.0481e-02, -4.7518e-02, -2.9176e-02,\n",
      "          5.3379e-02,  3.6485e-02,  2.7249e-02,  8.9362e-02,  1.3551e-01,\n",
      "          1.2192e-01,  7.6248e-02,  6.7578e-02,  1.4957e-02,  1.9122e-02,\n",
      "          1.0878e-01, -1.1958e-01, -4.5674e-01, -3.6380e-01, -1.7074e-02,\n",
      "         -1.8707e-03,  1.4117e-02,  3.0910e-02, -1.4756e-01, -9.9062e-02,\n",
      "          2.0997e-02, -5.5257e-02,  5.0444e-02, -9.3511e-03,  2.6703e-02,\n",
      "         -2.3001e-02,  1.5436e-02,  3.4552e-02, -7.1427e-03, -4.9771e-02,\n",
      "         -2.2559e-02,  5.1159e-02,  1.2768e-01,  1.4151e-01,  7.4483e-02,\n",
      "          1.0920e-01,  5.4920e-02,  1.2946e-01,  1.6555e-01, -6.3127e-02,\n",
      "         -4.3756e-01, -3.2589e-01,  2.6940e-02,  2.9855e-02,  1.3112e-01,\n",
      "         -3.2618e-02, -1.2145e-01, -1.3354e-01, -1.4028e-02,  4.7611e-02,\n",
      "          1.1951e-02, -1.8100e-02, -4.4368e-02, -8.6424e-03, -7.0884e-03,\n",
      "         -3.7835e-02, -3.4412e-02, -1.2533e-01, -1.7764e-01, -1.5535e-01,\n",
      "          6.7276e-03,  9.9001e-03,  3.4038e-02,  6.0387e-02,  1.0587e-01,\n",
      "          7.9271e-02,  9.2676e-02,  6.8374e-02, -3.1831e-01, -3.0116e-01,\n",
      "         -2.5967e-02,  3.2925e-02,  3.4604e-02, -3.1509e-02, -8.1217e-02,\n",
      "         -1.2427e-01,  2.2410e-02,  7.3454e-02,  2.9675e-02, -6.7251e-03,\n",
      "          1.2359e-02,  3.2445e-02,  1.7078e-02,  3.9502e-02, -2.9185e-02,\n",
      "         -2.5887e-01, -2.9568e-01, -2.2404e-01, -2.1304e-03,  1.6812e-02,\n",
      "          4.4586e-02,  2.8224e-02,  8.3896e-02,  7.2641e-02,  7.8431e-02,\n",
      "          9.8141e-02, -2.3376e-01, -2.0131e-01, -9.0468e-03, -6.1883e-02,\n",
      "          3.2189e-02,  1.0274e-02, -3.8199e-02, -1.4002e-02,  3.0766e-02,\n",
      "          1.0181e-01,  5.6594e-02,  8.0247e-02,  2.9021e-02,  1.1508e-01,\n",
      "          2.3181e-02, -2.0203e-02, -1.4727e-01, -2.1952e-01, -3.6650e-01,\n",
      "         -2.7439e-01, -2.9834e-02, -3.1790e-02, -4.3486e-02, -2.9323e-02,\n",
      "          2.9562e-02,  8.0747e-02,  9.1927e-02,  7.4349e-04, -7.2963e-02,\n",
      "         -6.2277e-02,  6.9381e-03,  2.6123e-02,  1.0109e-02, -5.5696e-02,\n",
      "         -1.7720e-01, -2.6117e-02,  1.4018e-01,  1.0353e-01,  1.4115e-01,\n",
      "          8.2793e-02,  5.2305e-02,  7.2254e-02,  4.5636e-02,  3.9821e-02,\n",
      "         -7.2549e-02, -3.3484e-01, -2.9723e-01, -1.9736e-01, -3.6633e-02,\n",
      "         -3.0330e-02, -3.7390e-02,  2.6912e-02,  6.7454e-02,  6.5768e-02,\n",
      "          1.2784e-01,  8.0710e-02, -6.5753e-02, -7.8177e-02,  1.7400e-02,\n",
      "          2.0492e-02,  3.2832e-02, -8.1523e-02, -2.2724e-01, -3.2703e-04,\n",
      "          8.3238e-02, -2.4835e-03,  1.4826e-01,  1.1591e-01,  9.1118e-02,\n",
      "          1.3405e-01,  2.1831e-02, -2.2212e-03, -2.1628e-01, -3.6133e-01,\n",
      "         -2.6533e-01, -9.6872e-02, -3.8469e-02, -4.4680e-03, -1.0899e-03,\n",
      "          5.1897e-02,  2.0517e-02,  1.2316e-02,  6.0684e-02,  5.0087e-02,\n",
      "         -6.8710e-02, -1.3210e-01,  5.3211e-03, -2.5811e-02, -1.7829e-02,\n",
      "         -3.8093e-03, -1.9459e-01,  2.1056e-02,  9.4351e-02,  6.3455e-02,\n",
      "          6.8691e-02,  9.4156e-02,  9.1678e-02,  9.5045e-02,  5.9830e-02,\n",
      "         -1.1694e-01, -3.7998e-01, -3.4589e-01, -1.4434e-01, -8.7111e-02,\n",
      "         -3.4203e-02, -3.0807e-02, -2.2818e-02,  6.5596e-02,  1.5724e-02,\n",
      "         -1.4880e-02,  3.9950e-02,  3.5547e-02, -1.6737e-01, -2.6588e-01,\n",
      "          7.2594e-02, -9.4125e-03, -2.0674e-02, -4.3343e-02, -1.8178e-01,\n",
      "          3.1695e-02,  8.1906e-02,  1.1156e-01,  8.1120e-02,  9.3123e-02,\n",
      "          5.9422e-02,  1.2203e-01,  5.2866e-02, -1.6078e-01, -2.9587e-01,\n",
      "         -2.8771e-01, -1.0799e-01, -2.1569e-02,  1.7428e-02, -4.7111e-03,\n",
      "          8.4639e-04, -2.8557e-03, -2.1580e-02,  2.3306e-02, -6.0631e-03,\n",
      "         -1.7860e-02, -1.0948e-01, -1.7978e-01,  8.9220e-02, -3.1063e-02,\n",
      "          2.5475e-02, -1.0295e-01, -3.3730e-01,  3.0861e-02, -1.3687e-02,\n",
      "          7.3079e-04,  4.0014e-02,  5.6369e-02,  9.1354e-02,  1.6283e-01,\n",
      "          6.5628e-02, -1.5155e-01, -2.5519e-01, -1.3919e-01, -3.7690e-02,\n",
      "         -3.2318e-02, -4.5375e-02, -6.2595e-02, -1.5471e-02, -8.9883e-02,\n",
      "         -7.4133e-03,  3.2842e-02, -5.5410e-02, -5.3374e-02, -1.0711e-01,\n",
      "         -3.8269e-02, -1.3967e-02, -1.9235e-02,  1.1854e-01,  1.3589e-02,\n",
      "         -2.2033e-01, -2.2407e-02, -1.7309e-02,  3.8524e-02,  5.0223e-02,\n",
      "          5.4243e-02,  3.7932e-02,  9.7939e-02,  8.7272e-02,  7.0915e-04,\n",
      "         -1.1039e-01, -9.3657e-02, -3.7364e-02, -3.6815e-02, -3.3499e-02,\n",
      "         -4.0893e-02, -8.6445e-02,  4.0901e-03,  2.6340e-03,  1.4549e-02,\n",
      "         -6.6556e-02, -4.8582e-02, -2.0531e-01, -1.7593e-02, -5.2493e-02,\n",
      "          2.6323e-02,  1.3252e-01, -3.4829e-02, -1.2128e-01, -2.1449e-02,\n",
      "          1.9809e-02,  4.6286e-02, -1.1862e-02,  6.5328e-02,  3.9028e-02,\n",
      "          1.6400e-01,  1.5819e-01,  4.1317e-02, -4.6319e-02, -7.4189e-02,\n",
      "         -8.3777e-02, -9.0405e-02, -3.0904e-02, -5.1399e-02, -8.4551e-02,\n",
      "         -6.0994e-02,  1.0864e-02, -3.5391e-02, -1.0899e-01, -1.0028e-01,\n",
      "         -1.0913e-01, -4.6357e-03,  1.2079e-02, -6.5810e-03,  9.1337e-03,\n",
      "         -1.2191e-01, -7.8281e-02, -8.8191e-02,  3.4670e-02,  1.8341e-02,\n",
      "         -1.3229e-03,  5.3481e-04,  1.6813e-02,  7.2769e-02,  1.2103e-01,\n",
      "          4.9292e-02, -4.4773e-03, -1.9745e-02, -5.2578e-02, -2.4465e-02,\n",
      "         -8.0023e-02, -5.7639e-02, -1.0916e-01, -7.3617e-02, -3.1409e-02,\n",
      "         -7.8685e-02, -8.8157e-02, -9.7392e-02, -1.1425e-01,  1.2459e-01,\n",
      "          2.7285e-02,  1.8672e-02, -1.4439e-02, -4.6915e-02, -1.6140e-01,\n",
      "         -2.4150e-01,  3.2378e-02,  3.7582e-02, -9.3179e-03,  2.4485e-02,\n",
      "          7.1428e-02,  5.6334e-02,  1.1372e-01,  7.8762e-02,  1.0539e-01,\n",
      "          2.5580e-02,  2.8701e-02, -4.9808e-02, -1.5280e-02, -9.8121e-02,\n",
      "         -1.2190e-02,  1.4572e-03, -9.3974e-03, -3.2993e-02, -3.2940e-02,\n",
      "         -1.0705e-01, -3.8663e-02,  4.0121e-02, -1.0428e-02, -1.9436e-02,\n",
      "         -3.4184e-02, -3.1593e-02, -1.8265e-01, -2.0556e-01, -2.4468e-02,\n",
      "          3.4783e-02, -1.9616e-02,  1.4916e-02,  7.2081e-02,  9.0791e-02,\n",
      "          8.9419e-02,  8.8952e-02,  5.9247e-02,  4.2861e-02,  3.0233e-02,\n",
      "         -2.1804e-02, -1.6397e-02, -4.0760e-02, -1.3057e-01, -1.4268e-01,\n",
      "         -9.7066e-02, -2.3799e-02, -9.5780e-02, -1.2500e-01, -5.8953e-02,\n",
      "          8.9245e-03,  3.3345e-02, -2.7927e-02, -2.2612e-02,  8.3011e-03,\n",
      "         -2.6115e-02, -4.8045e-02, -4.7276e-03, -2.7028e-03, -8.4707e-02,\n",
      "         -4.4244e-02,  3.4046e-02,  4.7205e-02, -1.5393e-02,  2.7071e-02,\n",
      "          2.5454e-02, -1.8719e-03, -4.3237e-02, -7.1467e-02, -4.3545e-02,\n",
      "         -8.7705e-02, -1.7682e-01, -2.3388e-01, -1.6674e-01, -8.0526e-02,\n",
      "         -1.2295e-01, -1.0451e-02, -2.7197e-02,  4.2795e-03,  3.1787e-02,\n",
      "         -4.4908e-03, -1.3487e-02,  2.7160e-02, -2.7162e-02, -1.1532e-01,\n",
      "         -1.9999e-01, -2.7475e-01, -2.8038e-01, -4.7284e-01, -4.3747e-01,\n",
      "         -5.3705e-01, -3.5972e-01, -4.1962e-01, -4.3017e-01, -4.3912e-01,\n",
      "         -4.2187e-01, -4.2385e-01, -4.3933e-01, -3.9624e-01, -2.9574e-01,\n",
      "         -2.8114e-01, -2.3670e-01, -1.9813e-01, -1.0779e-01, -5.9592e-02,\n",
      "          1.4077e-02, -1.8037e-02,  5.2241e-04, -2.8004e-02, -3.3539e-02,\n",
      "          3.5813e-03, -2.7316e-02,  3.4291e-02, -1.2815e-01, -1.4612e-01,\n",
      "         -2.4368e-01, -2.3291e-01, -3.5977e-01, -3.4843e-01, -3.1876e-01,\n",
      "         -2.5689e-01, -3.4882e-01, -3.3275e-01, -3.2163e-01, -3.1801e-01,\n",
      "         -2.2416e-01, -2.1977e-01, -1.4259e-01, -1.9944e-01, -1.8727e-01,\n",
      "         -1.3138e-01, -6.9571e-02,  2.2954e-02, -2.6263e-02,  1.6830e-02,\n",
      "         -2.6169e-03, -3.3159e-02, -1.0258e-02,  2.1717e-02, -2.3612e-02,\n",
      "         -2.3445e-02,  1.0197e-02,  9.7476e-03, -3.3888e-02, -4.7642e-02,\n",
      "         -6.5580e-02, -3.1238e-02, -1.8372e-02, -5.5808e-02, -1.1471e-01,\n",
      "         -1.5787e-01, -1.7846e-01, -1.4517e-01, -3.0251e-02,  5.1494e-03,\n",
      "          1.9837e-02,  6.0244e-02, -1.0393e-01, -8.6383e-02, -7.7106e-02,\n",
      "         -2.4286e-02, -2.9187e-03, -7.0417e-03,  3.8962e-03],\n",
      "        [-1.1945e-02,  2.0915e-02,  2.5106e-02, -2.9780e-02,  3.0519e-03,\n",
      "          5.3910e-03,  5.6408e-03,  1.4734e-02,  3.0291e-02, -7.7962e-03,\n",
      "         -2.6513e-02,  1.5592e-02,  9.9375e-03, -4.5423e-03,  3.5046e-02,\n",
      "         -1.8842e-02, -2.8862e-02, -4.8692e-03,  1.4881e-02,  4.5665e-05,\n",
      "         -1.5878e-03, -2.4380e-03, -3.3490e-02,  2.4345e-02, -2.3025e-02,\n",
      "          2.3542e-02,  2.5810e-02, -1.2519e-02,  7.4792e-03,  2.6177e-02,\n",
      "          1.7621e-02, -1.3660e-02,  1.7945e-02, -3.3784e-02, -1.5367e-02,\n",
      "         -3.8639e-02, -7.7697e-02, -1.2479e-01, -8.6948e-02, -1.5776e-02,\n",
      "          1.6567e-02,  1.5572e-02,  1.0829e-01,  9.6982e-02, -7.1430e-02,\n",
      "         -1.6805e-01,  2.6586e-02, -3.4114e-02,  3.1165e-02,  1.6173e-02,\n",
      "          1.1733e-03,  1.2069e-02,  3.3356e-03,  2.8911e-02,  1.0071e-02,\n",
      "          1.3459e-02,  7.7759e-03,  3.5402e-02, -1.5557e-02, -3.2724e-02,\n",
      "          1.2495e-02, -1.5362e-02,  1.8774e-02, -1.2274e-01, -1.5973e-01,\n",
      "         -2.0632e-01, -1.1335e-01, -1.4829e-01, -6.8293e-02, -7.9194e-02,\n",
      "          1.3559e-01,  2.3341e-01,  2.0440e-01, -3.3854e-02, -9.9902e-02,\n",
      "         -1.3900e-01, -1.5135e-01, -1.7415e-01, -7.4173e-02, -1.2449e-01,\n",
      "         -8.3320e-02, -2.4864e-02, -2.0419e-02,  1.2110e-02, -5.9325e-04,\n",
      "         -1.7237e-02,  2.0958e-02,  4.7998e-02, -2.5363e-03, -6.5675e-02,\n",
      "         -4.8230e-02, -6.7049e-02, -1.6177e-01, -2.4381e-01, -2.3596e-01,\n",
      "         -2.7338e-01, -3.3439e-01, -1.9135e-01, -1.9747e-01, -6.2871e-02,\n",
      "         -7.0420e-02, -9.7058e-02, -1.5044e-01, -1.8339e-01, -1.6282e-01,\n",
      "         -1.0717e-01, -1.3212e-01, -2.2003e-01, -1.5459e-01, -1.2988e-01,\n",
      "         -8.2256e-02, -3.2553e-02,  2.2655e-03,  9.1553e-04,  4.6050e-02,\n",
      "          4.7535e-02,  9.8914e-02, -1.6456e-01, -1.0428e-01, -3.8398e-02,\n",
      "         -1.7172e-01, -1.5528e-01, -2.9288e-03, -8.0741e-02, -1.2736e-01,\n",
      "         -2.5974e-02, -3.9882e-02,  3.0974e-02, -7.1390e-02, -1.0934e-01,\n",
      "         -8.5624e-02, -2.2303e-02,  1.0716e-02,  5.6782e-02,  2.7408e-02,\n",
      "          6.2822e-02, -2.1998e-01, -2.5239e-01, -1.2079e-01, -1.0314e-01,\n",
      "         -2.6089e-02, -3.1252e-02,  4.0475e-02,  8.4680e-02,  8.8859e-02,\n",
      "         -8.5822e-02, -1.6938e-01, -1.7413e-01, -4.0733e-01, -3.0466e-01,\n",
      "         -9.1564e-02, -9.5258e-02, -1.0035e-01, -1.2260e-01, -6.3362e-02,\n",
      "         -3.0031e-02, -1.1941e-01, -1.3123e-01, -7.2707e-02, -2.5826e-02,\n",
      "          1.8397e-03,  9.5698e-02,  1.6154e-01,  1.4612e-01,  4.0436e-02,\n",
      "         -3.4397e-01, -1.9027e-01, -1.1866e-01,  2.5567e-02,  2.8835e-02,\n",
      "          5.5301e-03,  1.1442e-01,  1.3251e-01, -1.9926e-01, -2.6467e-01,\n",
      "         -3.0650e-01, -3.6952e-01, -3.3573e-01, -9.8680e-02, -7.2411e-02,\n",
      "         -1.0928e-01, -1.0016e-01, -1.1776e-01, -1.4690e-01, -1.5040e-01,\n",
      "         -1.5344e-01, -6.3970e-02, -4.1902e-02,  2.3183e-02,  1.3078e-02,\n",
      "          2.0058e-02,  4.4821e-02, -8.0393e-02, -3.8169e-01, -8.4197e-02,\n",
      "         -3.9626e-02, -3.9798e-03, -3.8210e-02,  4.0785e-03, -3.7984e-02,\n",
      "          2.8455e-02, -2.1167e-01, -2.8958e-01, -2.7289e-01, -3.6277e-01,\n",
      "         -2.6008e-01, -1.2124e-01, -6.6831e-02, -3.6284e-02, -8.4326e-02,\n",
      "         -9.0000e-02, -1.6952e-01, -1.1164e-01, -1.2984e-01, -1.2253e-01,\n",
      "          1.2445e-02, -1.8187e-02, -2.8353e-02,  4.1097e-02, -1.6756e-02,\n",
      "         -2.5184e-01, -3.5536e-01, -6.0742e-02, -1.1850e-01,  4.0306e-03,\n",
      "         -3.7541e-02, -8.2018e-03, -1.3538e-03, -5.2297e-02, -4.7183e-02,\n",
      "         -1.9237e-01, -2.1273e-01, -3.1570e-01, -2.7639e-01, -8.7287e-02,\n",
      "         -1.1533e-01, -4.6577e-02, -1.2397e-02, -2.6764e-02, -3.4138e-02,\n",
      "         -8.0917e-02, -4.3902e-02, -1.7976e-02, -1.6054e-02, -6.2969e-02,\n",
      "         -7.6522e-02, -9.1158e-02, -2.2702e-01, -3.7034e-01, -3.0129e-01,\n",
      "         -1.4425e-01, -8.1823e-02,  2.5990e-02,  8.9097e-04, -3.1810e-02,\n",
      "         -1.4092e-02, -1.1796e-01, -1.1823e-01, -1.0231e-01, -1.1537e-01,\n",
      "         -2.9360e-01, -2.3902e-01, -9.2754e-02, -1.4183e-02, -9.8636e-02,\n",
      "          2.4719e-02,  1.3090e-01,  1.6474e-01,  8.7323e-02, -1.4267e-02,\n",
      "         -2.1368e-02, -9.0827e-02, -1.2061e-01, -1.2657e-01, -1.2767e-01,\n",
      "         -2.3730e-01, -2.5844e-01, -2.8861e-01, -3.6972e-02, -2.0930e-02,\n",
      "         -3.0183e-02,  2.4456e-02,  1.7227e-02, -6.1424e-02, -1.1961e-01,\n",
      "         -1.3182e-01, -2.0164e-01, -1.9691e-01, -1.4539e-01, -1.3279e-01,\n",
      "         -5.4525e-02, -3.0283e-02, -4.5545e-02,  6.8781e-02,  2.6434e-01,\n",
      "          2.8667e-01,  1.7910e-01, -5.4265e-03, -8.9253e-02, -4.9578e-02,\n",
      "         -1.1855e-01, -8.7010e-02, -1.0677e-01, -1.9848e-01, -2.0116e-01,\n",
      "         -5.8132e-02, -7.6215e-02,  7.1381e-02,  5.7314e-03,  1.2842e-04,\n",
      "          4.1108e-02, -1.1456e-02, -5.1104e-02, -7.2373e-02, -2.3468e-01,\n",
      "         -1.8000e-01, -1.7836e-01, -1.4406e-01, -6.8081e-02, -1.3453e-01,\n",
      "         -5.4223e-02,  9.7418e-02,  2.9265e-01,  2.9254e-01,  1.4514e-01,\n",
      "         -2.5687e-02, -4.1933e-02, -8.7007e-02, -8.5809e-02, -3.5803e-02,\n",
      "         -1.3562e-01, -1.4193e-01, -2.1336e-01,  1.5245e-02, -1.1057e-01,\n",
      "         -1.1496e-01,  2.8999e-02, -1.5747e-03, -2.8259e-02,  5.5979e-02,\n",
      "          1.7790e-02, -5.3509e-02, -6.1001e-02, -8.3622e-02, -1.3261e-01,\n",
      "         -1.0341e-01, -1.3856e-01, -1.8297e-01, -5.4066e-02,  1.0674e-01,\n",
      "          3.2555e-01,  2.5995e-01, -3.2559e-03, -1.9197e-02, -3.5227e-03,\n",
      "         -1.0616e-01, -8.9196e-02, -1.9120e-02, -1.2306e-01, -9.1687e-02,\n",
      "         -1.9861e-01, -1.3224e-01, -1.1397e-01, -1.2045e-01, -6.1823e-03,\n",
      "         -2.3310e-02, -3.1108e-02,  7.9304e-02, -1.4333e-02, -4.9580e-02,\n",
      "         -5.0346e-02, -1.2111e-01, -1.8485e-02, -1.2616e-01, -2.3172e-01,\n",
      "         -2.0735e-01, -6.5750e-02,  1.3642e-01,  3.9530e-01,  2.4434e-01,\n",
      "         -8.1843e-04,  2.6323e-02, -3.5465e-02, -1.7914e-01, -1.3818e-01,\n",
      "         -1.0982e-02, -1.6925e-01, -2.1513e-01, -2.2176e-01, -1.5078e-01,\n",
      "         -1.2098e-01,  3.5501e-02, -3.1008e-02, -3.5446e-02,  4.6443e-02,\n",
      "          6.2151e-02,  1.2126e-01, -6.6485e-02, -5.1048e-02, -1.3156e-01,\n",
      "         -7.6682e-02, -1.3220e-01, -2.5377e-01, -1.8652e-01, -2.7570e-02,\n",
      "          1.3629e-01,  4.1094e-01,  1.8326e-01,  1.8669e-02, -5.3170e-02,\n",
      "         -1.6655e-01, -1.9450e-01, -1.4761e-01,  1.0767e-02, -1.7727e-02,\n",
      "         -2.4118e-02, -1.8377e-02, -1.4420e-02,  1.5302e-02,  2.3006e-02,\n",
      "          8.9638e-03, -4.2429e-03,  8.5502e-02,  1.2328e-01,  2.8583e-02,\n",
      "         -1.1827e-01, -1.7263e-01, -6.5399e-02, -3.8338e-02, -1.5607e-01,\n",
      "         -1.2710e-01, -6.3869e-02, -1.4607e-02,  2.1937e-01,  3.5194e-01,\n",
      "          5.4580e-02,  2.7633e-02, -8.1365e-02, -2.2827e-01, -3.1353e-01,\n",
      "         -2.7847e-01, -4.0317e-02, -5.4501e-02, -1.4664e-01, -9.7297e-02,\n",
      "         -1.3361e-01, -8.3442e-02, -9.7902e-03, -2.2208e-02,  1.9002e-02,\n",
      "          5.4954e-02, -5.9560e-02, -4.0986e-02, -1.7320e-01, -1.5289e-01,\n",
      "         -8.0259e-02, -1.4914e-01, -2.4165e-02, -6.5920e-02, -7.8223e-02,\n",
      "          4.9484e-03,  1.7990e-01,  2.4930e-01, -1.3391e-03, -5.0500e-02,\n",
      "         -2.5167e-01, -2.6391e-01, -3.1667e-01, -7.9361e-02,  5.7736e-02,\n",
      "         -6.4957e-02, -1.7137e-01, -9.3622e-02, -2.0485e-01, -7.1651e-02,\n",
      "         -5.9745e-02,  5.3029e-03, -2.1111e-02,  1.3563e-02, -5.4393e-03,\n",
      "          7.9448e-02, -2.3639e-01, -2.6059e-01, -1.5221e-01, -4.1668e-02,\n",
      "         -7.3686e-02, -2.9193e-02, -1.0590e-01,  6.4375e-02,  1.6601e-01,\n",
      "          2.1007e-01, -3.0477e-02, -1.8281e-01, -2.2763e-01, -1.7928e-01,\n",
      "         -4.2999e-02, -1.3733e-02,  7.7203e-03, -4.0382e-02, -1.5639e-01,\n",
      "         -1.4548e-01, -2.0406e-01,  6.3403e-02, -2.7833e-02,  1.1774e-02,\n",
      "          1.4650e-02, -2.2180e-02, -5.4934e-02, -1.1347e-01, -4.3363e-01,\n",
      "         -3.8193e-01, -1.2851e-01, -6.9355e-02, -5.5934e-02, -9.2234e-02,\n",
      "         -7.3102e-02,  4.6541e-02,  1.5033e-01,  1.5497e-01, -7.7598e-02,\n",
      "         -2.1232e-01, -1.5005e-01, -6.8824e-04,  4.5473e-02, -1.2088e-02,\n",
      "         -5.5397e-02, -1.1807e-01, -1.5140e-01, -2.2089e-01, -8.6414e-02,\n",
      "          5.8334e-02,  4.4315e-02, -8.4452e-03, -2.7172e-02,  3.6691e-02,\n",
      "         -6.7631e-02, -2.2783e-01, -4.5836e-01, -2.1956e-01, -6.5873e-02,\n",
      "         -6.2651e-02, -1.6184e-02, -5.1650e-02,  2.7474e-03,  2.2967e-03,\n",
      "          6.0406e-02,  1.5540e-02, -3.8778e-02, -5.1053e-02, -1.2422e-02,\n",
      "         -2.3395e-02,  1.2826e-02, -2.4485e-02, -1.5933e-01, -1.4158e-01,\n",
      "         -7.0373e-02, -8.0353e-02, -1.0530e-01,  3.1701e-02,  1.0003e-01,\n",
      "         -2.5157e-02,  6.3480e-03, -5.8244e-02, -6.5489e-02, -8.6892e-02,\n",
      "         -1.9022e-01, -3.7800e-02, -2.1340e-02, -1.9284e-02,  4.9369e-03,\n",
      "          4.4586e-02,  2.0163e-02,  7.0606e-03, -4.1085e-02,  2.0454e-03,\n",
      "          1.9500e-02,  6.6781e-02,  2.0281e-02,  7.8462e-02, -9.1528e-02,\n",
      "         -1.7530e-01, -2.1974e-01, -2.0036e-01, -9.4277e-02, -2.5631e-02,\n",
      "         -7.0350e-02,  7.8528e-02,  4.1711e-02,  1.1971e-03, -1.2774e-02,\n",
      "         -6.9495e-02,  1.4332e-02,  5.7475e-02,  9.8011e-02,  3.4974e-02,\n",
      "         -3.1651e-02, -1.6839e-02, -3.5616e-02,  4.4859e-02, -6.9093e-02,\n",
      "         -1.0195e-01, -1.0342e-01, -1.0844e-02,  9.2026e-04,  9.1707e-02,\n",
      "          5.7788e-02,  9.7649e-02, -6.9744e-02, -1.7192e-01, -1.8422e-01,\n",
      "         -2.9683e-01, -1.2285e-01,  6.2172e-02,  3.9471e-02,  9.9621e-02,\n",
      "          1.5716e-02, -2.6456e-02, -3.1581e-02, -5.6073e-02,  5.3426e-03,\n",
      "          6.1538e-02,  2.7102e-01,  1.6148e-01,  1.1719e-02, -4.5754e-02,\n",
      "         -4.3603e-02, -5.2465e-02, -1.1061e-01, -1.6945e-01, -1.4160e-01,\n",
      "         -1.8268e-02, -2.9100e-03,  6.3396e-02,  1.5432e-01,  1.7451e-01,\n",
      "          3.4381e-02, -1.4116e-01, -2.7593e-01, -3.1491e-01, -1.6065e-01,\n",
      "         -6.7715e-02,  9.2522e-02,  6.2346e-02,  1.2400e-02,  2.1828e-02,\n",
      "         -2.7542e-02,  2.1283e-02, -1.8944e-01, -6.0026e-02,  1.8295e-01,\n",
      "          2.2134e-01,  7.7035e-02,  1.9155e-02, -7.4214e-03, -4.3273e-02,\n",
      "         -7.3021e-02, -2.6857e-02, -9.5866e-02, -5.4883e-02,  2.2542e-02,\n",
      "          1.3339e-01,  2.7894e-01,  5.7409e-02, -7.3140e-02, -2.1149e-01,\n",
      "         -2.9986e-01, -1.3505e-01, -2.0340e-01, -1.3275e-02,  5.3913e-02,\n",
      "          1.1094e-01,  4.2824e-03,  4.4205e-03,  1.8000e-03,  4.8124e-03,\n",
      "         -1.9926e-01, -1.4384e-01,  6.4599e-02,  6.2834e-02, -1.8806e-02,\n",
      "         -1.0262e-01, -5.7327e-02, -1.3701e-01, -1.3113e-01, -1.3724e-01,\n",
      "         -1.9626e-01, -1.6534e-01, -1.5739e-01, -2.3144e-01, -3.9347e-02,\n",
      "          6.7747e-02,  6.2773e-02, -1.3641e-01,  8.2484e-02,  5.4395e-02,\n",
      "         -3.2596e-02, -5.6155e-02,  1.1880e-01,  3.6301e-02, -2.1929e-02,\n",
      "         -2.7319e-02, -1.7108e-02,  3.6674e-03, -2.7005e-02, -1.7244e-01,\n",
      "         -2.1792e-01, -1.8469e-01, -3.0883e-01, -1.9844e-01, -3.0846e-01,\n",
      "         -4.3648e-01, -4.3768e-01, -3.5157e-01, -2.9257e-01, -2.7825e-01,\n",
      "         -4.3550e-01, -5.0211e-01, -3.5960e-01, -2.8303e-01, -2.3796e-01,\n",
      "         -5.4028e-02, -2.1609e-03, -1.9114e-02,  3.4648e-02, -2.0923e-02,\n",
      "         -2.3872e-03,  3.3131e-02,  9.8909e-04, -1.4200e-02, -1.6026e-02,\n",
      "          3.2431e-02,  1.2614e-02, -8.3955e-02, -1.6039e-01, -1.8857e-01,\n",
      "         -1.0288e-01, -1.3803e-01, -2.4043e-01, -2.6975e-01, -3.6324e-01,\n",
      "         -4.1206e-01, -4.0379e-01, -3.2416e-01, -3.1006e-01, -2.1836e-01,\n",
      "         -1.8533e-01, -1.5788e-01, -1.1143e-01,  2.9185e-02,  2.4860e-02,\n",
      "          3.5141e-02, -2.6653e-02, -7.3506e-03,  3.0315e-02, -3.4450e-02,\n",
      "          1.3969e-02, -3.1711e-02, -1.2321e-04,  3.2051e-02,  3.5699e-02,\n",
      "         -3.4462e-02, -1.5874e-02, -1.9436e-02,  2.5730e-02, -3.3602e-02,\n",
      "          4.0171e-03, -6.0963e-02, -9.3090e-02, -3.2942e-02, -4.4484e-02,\n",
      "         -2.1924e-02, -2.7717e-02, -9.3658e-03, -8.2311e-02, -9.5297e-02,\n",
      "         -4.9746e-02,  1.0347e-03,  5.3556e-03, -5.1807e-03,  3.5261e-02,\n",
      "         -2.1189e-02, -5.6574e-03, -8.7843e-03,  6.8568e-03],\n",
      "        [ 2.0426e-02,  8.1230e-03,  6.0587e-03,  1.2750e-02,  3.4700e-02,\n",
      "         -2.1730e-02,  1.1954e-03,  3.9925e-02,  2.4832e-02, -2.7075e-02,\n",
      "         -5.6737e-03, -3.0488e-02,  4.2288e-03, -1.8546e-02, -1.5743e-02,\n",
      "          1.3217e-02, -3.3425e-02, -4.7926e-03, -1.7366e-02, -1.6942e-02,\n",
      "         -3.4431e-02, -1.7692e-02, -1.7139e-02, -1.8238e-02,  3.0558e-02,\n",
      "         -4.4416e-03, -1.4319e-02, -2.6925e-02, -1.4715e-02,  2.1516e-02,\n",
      "          1.3378e-02,  1.8352e-02, -3.4589e-02, -1.5264e-02,  4.7147e-04,\n",
      "         -5.5367e-02, -1.5252e-02, -1.4830e-01, -1.0603e-01, -8.9350e-02,\n",
      "         -2.1387e-01, -1.4322e-01, -8.5580e-02, -2.3456e-02,  1.6045e-01,\n",
      "         -7.3956e-02, -1.7279e-01, -2.6015e-01, -1.6421e-01, -7.3601e-02,\n",
      "         -2.3609e-02, -1.9181e-03, -2.8378e-02,  2.7729e-02,  2.1143e-02,\n",
      "          3.5473e-02, -1.8973e-02, -3.6206e-04,  1.1804e-03, -4.0640e-03,\n",
      "         -5.9815e-02, -5.5107e-02, -3.8359e-02, -9.2379e-02, -1.2011e-01,\n",
      "         -6.6394e-02, -8.8032e-02, -9.6664e-02, -8.8353e-02, -5.1311e-02,\n",
      "         -1.2932e-01, -1.4299e-01, -9.5176e-02, -1.3467e-01, -1.8087e-01,\n",
      "         -2.3582e-01, -1.9635e-01, -1.9188e-01, -1.8124e-01, -8.1498e-02,\n",
      "          4.7935e-02,  7.6519e-02,  3.5068e-02, -1.0735e-03,  3.3850e-02,\n",
      "          3.2632e-02, -2.5547e-02, -9.6945e-03, -4.1875e-02,  3.8791e-02,\n",
      "          3.8854e-02,  9.4442e-02,  1.0415e-01,  1.6114e-01,  1.0831e-01,\n",
      "          1.0116e-01,  7.8771e-02,  6.6513e-02,  4.7773e-03, -4.8422e-02,\n",
      "         -2.7428e-03, -6.5073e-02, -1.0946e-01, -6.8756e-02, -8.5571e-02,\n",
      "         -3.9975e-02, -1.0600e-01, -2.9581e-01,  3.7842e-04,  7.9302e-02,\n",
      "         -2.4691e-03,  1.4948e-02, -1.9065e-02, -3.2960e-02, -6.2774e-02,\n",
      "         -1.2512e-01, -8.5647e-02, -3.6920e-02,  3.4836e-02,  6.8539e-02,\n",
      "          1.5680e-02,  1.2980e-01,  7.8698e-02,  7.3676e-02,  8.5473e-02,\n",
      "          8.6744e-02,  6.3334e-02,  1.0177e-02,  1.7028e-02, -1.6719e-02,\n",
      "         -3.6173e-02, -7.3277e-02, -5.1185e-02, -1.0128e-01, -1.7290e-01,\n",
      "         -3.5888e-01, -1.9775e-01,  1.5796e-02, -1.2705e-01, -9.1083e-02,\n",
      "          2.8486e-02, -2.5979e-03, -7.9496e-03,  2.1299e-02, -2.7500e-02,\n",
      "         -9.8987e-02, -4.8103e-02,  1.2062e-02,  5.0086e-02,  1.6680e-02,\n",
      "          3.5317e-02,  9.3779e-02,  6.8991e-02,  1.1161e-01,  3.9446e-02,\n",
      "          7.6581e-02,  1.2154e-01,  7.8123e-02,  6.4017e-02, -2.1735e-03,\n",
      "         -2.4676e-02, -6.4239e-03, -2.9681e-02, -1.6562e-01, -1.5646e-01,\n",
      "          6.0554e-02, -9.3737e-02, -1.0564e-01, -3.1863e-02,  1.0578e-02,\n",
      "          9.4375e-03, -1.0062e-01, -1.9163e-02,  8.9834e-03,  2.9733e-02,\n",
      "          6.0401e-02, -1.0825e-03,  2.7036e-02,  2.1444e-02, -3.6089e-04,\n",
      "          1.9950e-02,  6.2765e-02,  3.6003e-02,  1.6558e-02,  3.4475e-02,\n",
      "          1.3686e-02, -3.1291e-02, -6.1448e-02, -1.7299e-02, -1.7430e-02,\n",
      "         -3.6093e-02, -1.7702e-01, -2.4854e-01, -1.1582e-01, -9.8220e-02,\n",
      "         -1.4907e-02, -9.8073e-04, -5.9896e-03,  8.8720e-02, -2.0770e-02,\n",
      "         -2.8602e-02, -1.3121e-02,  2.9115e-02, -2.0522e-02,  2.7102e-02,\n",
      "         -2.6977e-02,  9.8657e-03,  3.5988e-02, -2.4690e-02,  3.5253e-02,\n",
      "          3.5528e-02, -1.8504e-02, -3.1779e-02, -8.5730e-02, -9.5344e-03,\n",
      "         -1.6422e-02, -4.3181e-02,  4.3576e-02,  4.3853e-02, -1.9773e-01,\n",
      "         -3.2258e-01, -1.6600e-01, -1.0138e-01, -2.6557e-02, -3.0180e-02,\n",
      "          1.1745e-01,  3.5676e-02, -1.8574e-02,  1.6797e-02,  3.1306e-02,\n",
      "         -4.8035e-02, -2.4614e-02, -2.8968e-03, -5.4052e-03,  7.9467e-02,\n",
      "          5.8466e-02,  8.4996e-03,  4.7815e-03,  3.2830e-02, -8.1064e-03,\n",
      "         -1.0481e-02, -1.7094e-02, -1.3233e-02, -9.6907e-03, -1.0634e-02,\n",
      "         -1.1758e-02,  1.9123e-02, -1.4965e-02, -4.6298e-01, -2.1001e-01,\n",
      "         -1.2710e-01, -2.8301e-02,  1.8843e-02, -9.7796e-02, -1.5677e-01,\n",
      "         -4.2407e-02,  3.5253e-02,  8.6458e-02, -6.4776e-03,  4.0539e-02,\n",
      "          2.0359e-03,  6.4869e-02,  5.1165e-02,  5.4656e-02,  3.7852e-02,\n",
      "          4.3715e-02,  6.5899e-02,  1.5964e-02,  2.6134e-02,  4.4667e-02,\n",
      "          4.8853e-02, -3.9574e-03,  8.6941e-04, -5.3965e-02,  3.3814e-03,\n",
      "         -2.6364e-02, -2.9925e-01, -1.1906e-01, -1.3133e-01, -1.1830e-03,\n",
      "          3.0266e-02, -9.2704e-02, -1.5750e-01, -6.9322e-02, -3.6069e-02,\n",
      "          1.1054e-01,  1.5553e-01,  1.5875e-02,  6.4034e-03,  4.0844e-02,\n",
      "          1.9007e-02, -4.1712e-02, -5.3908e-02, -3.4527e-02,  3.6382e-03,\n",
      "         -3.2667e-02,  2.6476e-02,  5.5586e-02,  4.0893e-02, -1.8076e-03,\n",
      "         -4.3925e-02, -3.1856e-02, -3.2053e-02, -4.4949e-02, -4.9948e-02,\n",
      "         -1.1814e-01, -2.0043e-01, -5.1981e-03,  2.5374e-02, -1.0661e-01,\n",
      "         -1.9175e-01, -2.4171e-01,  2.6239e-02,  1.1522e-01, -8.4145e-02,\n",
      "         -2.1161e-01, -2.3029e-01, -2.3970e-01, -3.1076e-01, -2.6789e-01,\n",
      "         -2.6835e-01, -3.4495e-01, -2.3182e-01, -1.1801e-01, -8.4842e-04,\n",
      "         -4.1072e-02, -2.5142e-02,  3.0812e-02, -3.1079e-02, -1.6049e-02,\n",
      "         -1.2422e-02, -5.2817e-02,  1.7024e-02,  3.9679e-02, -1.3705e-01,\n",
      "         -9.5159e-02, -2.9991e-02, -1.8574e-02, -1.4077e-01, -2.0423e-01,\n",
      "         -1.0932e-01, -2.4196e-01, -5.7714e-01, -6.0359e-01, -4.8934e-01,\n",
      "         -4.1786e-01, -3.7640e-01, -3.3855e-01, -2.9327e-01, -2.4784e-01,\n",
      "         -2.6349e-01, -1.7080e-01, -1.0669e-01, -1.1678e-01, -8.2195e-03,\n",
      "          4.5113e-02, -1.2754e-02,  1.1880e-02, -9.6270e-02, -1.7571e-01,\n",
      "          5.9568e-02,  1.8014e-01,  2.0717e-01,  5.2557e-02, -2.1563e-02,\n",
      "          2.5377e-02,  5.0628e-03, -2.0582e-01, -3.2658e-01, -5.3105e-01,\n",
      "         -5.6907e-01, -4.1772e-01, -3.0224e-01, -1.4099e-01, -9.1406e-02,\n",
      "         -3.8823e-02, -2.0414e-02, -9.2275e-02, -7.2178e-02, -8.1981e-02,\n",
      "         -1.1009e-01, -7.2560e-02, -5.4563e-02, -3.5433e-02,  9.2412e-03,\n",
      "         -5.9483e-02, -9.3940e-02, -6.3049e-02,  9.6424e-02,  2.5234e-01,\n",
      "          4.1051e-01,  2.2409e-01, -2.3185e-02, -7.5830e-02, -7.3662e-02,\n",
      "         -1.7080e-01, -2.3711e-01, -3.4760e-01, -2.3646e-01, -2.7556e-02,\n",
      "          7.3331e-03,  6.7617e-02,  5.0064e-02,  8.1763e-02,  3.4702e-02,\n",
      "          5.9380e-02,  8.3438e-03, -4.0151e-02, -3.3780e-02, -6.9714e-02,\n",
      "         -2.6812e-02, -3.6061e-02, -3.6817e-02, -1.0495e-01, -5.7816e-02,\n",
      "         -1.2233e-01, -5.3553e-02,  1.9965e-01,  3.7320e-01,  1.4404e-01,\n",
      "          6.1462e-03, -9.2524e-02, -1.2555e-01, -5.6192e-02, -3.7524e-02,\n",
      "          9.7798e-02,  8.2514e-02,  6.0416e-02,  5.8058e-02,  2.3985e-02,\n",
      "          4.6899e-02,  3.6134e-02, -8.8378e-03,  9.1905e-02,  4.0072e-02,\n",
      "         -3.9499e-02,  3.1549e-02, -3.4214e-02, -9.6633e-02, -5.1637e-03,\n",
      "         -6.7711e-02, -3.5539e-02, -8.7156e-02, -5.8909e-02,  3.2356e-02,\n",
      "          2.2634e-01,  4.5253e-01,  1.7878e-01, -3.4540e-02, -9.6611e-02,\n",
      "         -1.6235e-01,  1.9309e-02,  7.5178e-02,  1.5521e-01,  1.6579e-01,\n",
      "          5.6792e-02,  3.1299e-02,  5.8243e-02,  6.4957e-02,  6.5913e-02,\n",
      "          8.2141e-02,  9.1097e-02,  1.7099e-02,  2.3091e-03,  6.0012e-02,\n",
      "          3.9608e-02, -2.9190e-02, -3.3642e-02, -3.8489e-02,  2.9490e-02,\n",
      "          6.1219e-02, -3.0372e-03,  7.0554e-02,  3.0810e-01,  5.1802e-01,\n",
      "          2.2401e-01,  3.5530e-03, -1.3255e-01, -1.3592e-01,  4.3535e-02,\n",
      "          8.2017e-02,  1.3185e-01,  1.3042e-01,  9.6236e-02,  7.5359e-02,\n",
      "          6.3766e-02,  7.6493e-02,  7.6113e-02,  1.0315e-01,  1.4246e-01,\n",
      "          7.8991e-02,  9.0313e-02,  3.0392e-02,  1.4997e-02,  2.4987e-02,\n",
      "         -6.6181e-02, -4.1016e-02, -7.1838e-03,  3.1450e-02,  4.4333e-02,\n",
      "          1.4935e-01,  3.7961e-01,  4.1760e-01,  2.7005e-01,  3.6913e-03,\n",
      "         -4.9667e-02, -2.3960e-01, -5.4890e-02,  3.1457e-02,  1.4931e-01,\n",
      "          1.0008e-01,  1.6862e-01,  1.0842e-01,  8.9869e-02,  7.9266e-02,\n",
      "          6.9268e-02,  1.5707e-01,  1.3453e-01,  9.1700e-02,  9.8184e-02,\n",
      "          8.3856e-02,  6.0979e-02,  1.6244e-03,  1.0296e-02, -1.3355e-02,\n",
      "          2.7524e-02,  5.2582e-02,  1.2373e-01,  2.8037e-01,  3.6819e-01,\n",
      "          2.5710e-01,  1.6158e-01, -5.3179e-03, -3.2024e-02, -1.6655e-01,\n",
      "         -1.4241e-01,  7.4289e-02,  8.0300e-03,  4.5485e-02,  1.1460e-01,\n",
      "          5.6822e-02,  9.7232e-02,  7.4437e-02,  9.0894e-02,  1.3064e-01,\n",
      "          9.5161e-02,  5.5941e-02,  3.4610e-02,  1.5906e-02,  2.4233e-02,\n",
      "          5.8443e-04, -5.5961e-02,  2.8322e-02,  2.6547e-02,  5.5708e-02,\n",
      "          8.0046e-02,  2.7695e-01,  3.7526e-01,  2.2263e-01,  7.0636e-02,\n",
      "         -1.1811e-02,  1.8459e-03, -4.9854e-02, -1.4528e-02,  6.2111e-02,\n",
      "          6.3164e-02,  6.0793e-02,  1.0171e-01,  1.0099e-01,  7.7558e-02,\n",
      "          8.4698e-02,  8.0389e-02,  3.9831e-02,  9.8597e-03,  3.0933e-04,\n",
      "          2.3361e-02, -9.8389e-03, -5.5037e-02,  3.1072e-02,  7.1728e-02,\n",
      "          3.9701e-02,  7.8177e-03,  3.5490e-02,  1.5561e-01,  2.5057e-01,\n",
      "          3.2551e-01, -2.1388e-02, -4.2883e-02, -3.4125e-02, -2.3284e-02,\n",
      "         -8.5150e-02, -1.1557e-02,  6.1164e-02, -9.7844e-03, -2.5850e-02,\n",
      "          8.2995e-04,  9.3127e-03,  8.5011e-02,  5.3294e-02,  3.8761e-02,\n",
      "          2.2685e-02, -5.0212e-02, -4.3862e-02, -5.2573e-02,  3.1835e-02,\n",
      "          2.4466e-02,  6.5596e-02,  7.1436e-02,  3.9658e-02,  3.7214e-02,\n",
      "          6.8382e-02,  2.3621e-01,  2.2264e-01,  2.4717e-01, -4.5679e-03,\n",
      "         -6.7085e-04, -1.9440e-02,  3.5342e-02, -3.8724e-02,  5.4868e-04,\n",
      "          9.5462e-02,  3.0264e-02,  6.6279e-03,  6.3797e-02,  9.0444e-02,\n",
      "          5.0299e-02,  4.1470e-02,  7.0438e-02,  2.9084e-02,  2.2879e-02,\n",
      "         -1.8785e-02, -8.9235e-02, -3.2184e-02,  8.4967e-03,  9.4363e-02,\n",
      "          5.3267e-02,  8.2083e-02,  7.9343e-02,  1.6425e-01,  2.2777e-01,\n",
      "          2.3063e-01,  1.4052e-01, -1.7973e-01, -1.6909e-02,  2.6256e-02,\n",
      "         -3.4759e-02, -5.6568e-02, -2.8943e-02, -4.9074e-03, -3.4457e-02,\n",
      "          5.5388e-02,  1.0249e-02, -3.5988e-02, -8.1446e-02, -6.4173e-02,\n",
      "         -5.2121e-02, -1.2434e-02, -3.1466e-02, -1.1778e-02, -7.7531e-02,\n",
      "         -4.8528e-02, -1.7978e-02,  8.6264e-02,  1.4358e-01,  1.0588e-01,\n",
      "          1.6217e-01,  1.5002e-01,  1.6854e-01,  1.9117e-01,  1.7554e-01,\n",
      "         -6.7836e-02, -3.1243e-02, -7.9905e-03, -2.1515e-02, -3.2037e-02,\n",
      "         -3.2209e-01, -2.0498e-01, -1.3108e-01, -1.9285e-01, -1.1440e-01,\n",
      "         -5.1071e-02, -7.4271e-02, -8.6340e-02, -2.4828e-02,  1.4024e-02,\n",
      "         -2.7612e-02,  3.7311e-02, -5.6985e-02, -6.4904e-02, -5.8720e-02,\n",
      "          5.6186e-02,  5.5878e-02,  9.4583e-02,  1.2356e-01,  9.0078e-02,\n",
      "          6.9354e-02,  2.3715e-01,  2.3760e-01,  7.6696e-02,  1.2845e-02,\n",
      "          7.2311e-03,  5.9944e-03, -5.5401e-03, -1.7307e-01, -1.7798e-01,\n",
      "         -3.0825e-01, -3.8678e-01, -4.6388e-01, -5.5771e-01, -5.9959e-01,\n",
      "         -5.9690e-01, -5.1578e-01, -4.7948e-01, -4.8320e-01, -3.0188e-01,\n",
      "         -2.2963e-01, -1.8897e-01, -1.7825e-01, -2.8264e-01, -2.5919e-01,\n",
      "         -1.2880e-01, -2.0742e-01, -3.4876e-01, -2.3479e-01, -1.2920e-01,\n",
      "          4.2197e-02,  6.7957e-02, -2.4701e-03, -3.2232e-02, -5.1006e-04,\n",
      "          1.9929e-02,  1.7699e-02, -6.9463e-02, -1.1100e-01, -1.1048e-01,\n",
      "         -2.0004e-01, -2.0516e-01, -9.1778e-02, -1.0504e-01, -1.8809e-01,\n",
      "         -1.7117e-01, -1.5006e-01, -1.9757e-01, -2.7517e-01, -3.1157e-01,\n",
      "         -2.1696e-01, -1.6392e-01, -1.6186e-01, -1.8428e-01, -2.0199e-01,\n",
      "         -1.8315e-01, -3.0346e-03,  8.3407e-03,  9.6133e-03,  2.1328e-02,\n",
      "          2.8348e-02, -1.0298e-02,  1.6142e-02,  1.7255e-02, -2.0639e-03,\n",
      "         -2.0387e-02, -9.9577e-04, -1.5441e-02,  2.2655e-02,  3.5369e-03,\n",
      "          1.0490e-02, -5.0684e-02, -2.2137e-02, -4.4414e-02, -2.4248e-02,\n",
      "         -2.2939e-02,  1.9166e-02,  1.6131e-02, -2.2409e-02,  2.3942e-02,\n",
      "          3.2773e-02,  2.2176e-02, -1.1166e-01, -1.2234e-01, -3.1653e-02,\n",
      "         -2.1316e-02,  3.4331e-02,  3.3508e-02, -1.4668e-03],\n",
      "        [-3.1117e-02,  3.5583e-02,  6.8858e-04,  3.2824e-02, -2.3094e-02,\n",
      "         -9.3524e-03,  3.1332e-03,  1.5537e-02,  1.0881e-02, -7.2113e-03,\n",
      "         -3.2867e-02,  3.3252e-02, -5.5234e-03, -1.0735e-02, -2.2454e-03,\n",
      "          4.1451e-03,  1.9028e-02,  1.0852e-02, -1.5962e-02,  2.0983e-02,\n",
      "         -2.1330e-02, -1.0086e-03,  1.0156e-02,  1.6529e-02, -2.7665e-02,\n",
      "         -1.6988e-02, -6.8851e-05, -3.5128e-03, -1.2596e-02,  2.5325e-02,\n",
      "         -6.7682e-03, -1.1538e-02, -1.5593e-02, -2.4097e-02,  3.8279e-04,\n",
      "         -2.2086e-02,  1.0149e-02,  1.7418e-02,  3.5294e-02,  3.3859e-02,\n",
      "         -7.4393e-03,  3.4714e-03, -7.6983e-02, -9.8801e-02, -1.0718e-01,\n",
      "          1.8764e-02, -4.5931e-03, -3.2782e-02, -2.6169e-02,  9.0010e-03,\n",
      "         -1.1433e-02,  2.8873e-02, -2.5888e-02,  6.8151e-03,  5.2730e-03,\n",
      "         -1.1066e-02,  1.3990e-02,  1.4626e-02, -2.6253e-02, -4.9376e-03,\n",
      "         -4.6964e-03,  7.0605e-03, -3.7167e-03, -5.4003e-02, -8.3791e-02,\n",
      "         -1.0782e-01, -9.5614e-02, -3.8522e-02,  2.9822e-03,  6.9976e-02,\n",
      "         -3.3330e-03, -1.1146e-01, -2.3138e-01, -1.6669e-01, -1.2885e-01,\n",
      "         -1.8162e-01, -7.5125e-02, -1.3442e-01, -1.1853e-01,  2.3039e-02,\n",
      "         -3.0718e-02,  3.4900e-02,  3.5165e-02, -2.0475e-03,  2.1013e-02,\n",
      "          2.0592e-02,  1.9743e-02, -2.9982e-02,  9.3791e-02,  8.0541e-02,\n",
      "         -8.0882e-02, -2.3850e-02, -7.3067e-02, -6.3813e-02, -8.9590e-02,\n",
      "          4.5149e-02,  9.6793e-02,  1.2984e-02,  6.9416e-02,  3.7821e-02,\n",
      "          3.5099e-02,  1.5761e-01,  1.2571e-01,  1.0395e-01,  1.1767e-01,\n",
      "         -8.9797e-02, -2.1897e-01, -2.0102e-01, -9.5810e-02, -4.8388e-03,\n",
      "          9.7259e-03,  1.1416e-02, -1.1661e-02,  2.0378e-02,  7.9330e-02,\n",
      "          6.3503e-02,  7.7210e-02,  1.6839e-01,  1.0684e-01,  7.9432e-02,\n",
      "          9.0781e-02,  9.0686e-02,  6.9891e-02,  7.1921e-02,  9.2446e-02,\n",
      "          5.0365e-02,  3.5043e-02,  5.6515e-02,  3.9346e-02, -8.0529e-03,\n",
      "          6.8157e-02,  2.9965e-02, -8.7521e-02, -1.0352e-01, -1.2124e-01,\n",
      "         -4.4924e-01, -1.0632e-01,  2.9917e-02, -2.6071e-02, -9.2438e-03,\n",
      "          1.7600e-02,  2.4559e-02, -6.7514e-02,  1.7928e-01,  2.0908e-01,\n",
      "          1.2351e-01,  1.7360e-01,  8.0931e-02,  4.7522e-02,  5.6909e-02,\n",
      "          4.2551e-02,  5.2171e-02,  2.8047e-02,  4.3009e-02, -1.6737e-02,\n",
      "          3.0823e-02,  3.8067e-02, -5.3944e-02, -5.8723e-02, -4.1488e-03,\n",
      "         -3.1808e-02, -1.4411e-01, -1.9107e-01, -2.8954e-01, -1.9890e-01,\n",
      "         -1.1751e-01,  1.8519e-02,  2.6660e-02,  7.1567e-03,  6.5611e-02,\n",
      "          2.1149e-02,  2.3879e-01,  1.3637e-01,  5.7004e-02,  9.5953e-02,\n",
      "         -2.1860e-02,  2.6987e-03,  5.8654e-02,  6.7846e-02,  6.0035e-02,\n",
      "          3.6502e-02,  7.1336e-02,  1.9034e-02,  4.5465e-02,  6.3125e-02,\n",
      "          1.8060e-02,  2.3890e-02, -7.6526e-02, -1.2888e-02, -5.5120e-02,\n",
      "         -4.4981e-02, -1.2631e-01, -3.0901e-01, -1.6464e-01, -3.0386e-03,\n",
      "         -2.4063e-02, -2.6090e-02,  1.7206e-01, -1.2088e-01,  1.3286e-01,\n",
      "          1.0751e-01,  8.1744e-02,  4.4483e-03, -1.2056e-02, -4.2275e-03,\n",
      "         -1.2650e-02, -1.5358e-02, -3.1016e-02,  4.1004e-02,  3.0824e-02,\n",
      "          7.3111e-02,  3.5878e-02,  3.3852e-02,  2.1809e-02, -1.0240e-03,\n",
      "         -2.2162e-02,  6.9709e-03, -4.6718e-02, -1.0642e-01, -2.2424e-01,\n",
      "         -4.4909e-01, -1.9856e-01, -2.5154e-03, -5.1128e-03, -3.0235e-02,\n",
      "          1.3299e-02, -1.8629e-01, -6.9279e-02,  1.4735e-01,  4.4982e-02,\n",
      "         -1.1402e-02, -1.1956e-02,  2.5279e-02, -3.6362e-02,  1.6806e-02,\n",
      "         -1.9023e-02,  2.1325e-02, -2.4067e-02,  5.8476e-02,  3.9261e-02,\n",
      "          8.4614e-03,  5.5400e-02,  7.3253e-02,  5.5414e-02,  2.0641e-02,\n",
      "         -3.6279e-03,  1.7452e-03, -3.2409e-01, -5.4770e-01, -2.7027e-01,\n",
      "          1.5904e-02,  1.9126e-02, -2.5073e-02,  1.9949e-02, -1.9065e-01,\n",
      "         -2.7935e-03,  1.2351e-01,  5.1482e-02,  5.7324e-02,  5.3402e-02,\n",
      "         -2.9990e-02, -1.8019e-02, -9.9685e-02, -1.9749e-01, -1.2039e-01,\n",
      "         -3.6159e-02,  6.7833e-02,  1.1394e-01,  7.2274e-02,  5.3648e-02,\n",
      "          3.8025e-02,  2.3967e-02,  1.1384e-01,  8.7572e-02,  6.6827e-02,\n",
      "         -2.2426e-01, -5.3170e-01, -1.4879e-01, -4.1945e-02, -1.9799e-02,\n",
      "          2.7885e-02, -9.0208e-02, -2.0396e-01,  1.0998e-01,  1.1722e-01,\n",
      "          5.6690e-03,  3.2979e-02,  8.9644e-04, -1.2448e-01, -1.9140e-01,\n",
      "         -2.3126e-01, -2.1101e-01, -1.7107e-01,  2.3149e-02,  1.5289e-01,\n",
      "          1.0388e-01,  1.2403e-01,  9.6940e-02,  4.9475e-02,  6.5897e-02,\n",
      "          7.4982e-02,  1.2429e-01,  6.6629e-02, -1.0153e-01, -3.3969e-01,\n",
      "         -2.5440e-02,  1.2969e-02, -2.4574e-02,  1.7193e-02, -6.1041e-02,\n",
      "         -1.4088e-01,  1.7470e-01,  7.6763e-02, -1.4085e-01, -1.3834e-01,\n",
      "         -2.0925e-01, -2.3030e-01, -1.8493e-01, -1.5313e-01, -7.2148e-02,\n",
      "          6.0620e-02,  5.1744e-02,  1.4033e-01,  8.0903e-02,  5.2137e-02,\n",
      "          7.5778e-02,  9.5570e-02,  9.6101e-02,  7.4641e-02, -5.9286e-02,\n",
      "         -1.1491e-01, -1.6635e-01, -2.6604e-01,  4.3415e-02,  2.8033e-02,\n",
      "          3.3388e-02, -2.4963e-02, -1.1412e-01, -1.9357e-01,  1.3549e-02,\n",
      "         -2.2110e-02, -1.7068e-01, -1.9054e-01, -1.2890e-01, -1.4814e-01,\n",
      "         -5.3322e-02,  4.0591e-03,  7.9116e-03,  6.7996e-02,  4.9429e-02,\n",
      "          1.3911e-01,  9.8652e-02,  7.6730e-02,  6.4077e-02,  5.9005e-02,\n",
      "         -3.3998e-02, -7.4475e-02, -2.7932e-01, -3.1412e-01, -3.2319e-01,\n",
      "         -1.8752e-01,  7.6043e-02, -3.3705e-02,  5.0223e-03,  2.3285e-02,\n",
      "         -5.1625e-02, -1.7327e-01,  3.5211e-02, -7.8389e-02, -9.3337e-02,\n",
      "         -6.6543e-03, -1.0014e-01, -4.5761e-02, -2.6139e-02, -9.0380e-02,\n",
      "         -1.8010e-03,  7.3372e-02,  6.1007e-02,  1.2352e-01,  5.4578e-02,\n",
      "          1.0008e-01,  1.2178e-02, -1.0290e-02, -1.3002e-01, -1.6401e-01,\n",
      "         -1.3460e-01, -2.8920e-01, -1.5828e-01, -2.3059e-01, -2.9386e-02,\n",
      "         -6.6909e-02, -9.3249e-02,  1.2220e-02,  6.7664e-02, -2.4394e-02,\n",
      "          2.6010e-02,  3.6402e-04, -1.0094e-01, -8.9639e-02, -3.5800e-02,\n",
      "         -3.2668e-02, -3.4884e-02, -1.3943e-01, -2.6133e-02,  5.9870e-02,\n",
      "          2.3564e-02,  6.6444e-02,  1.9164e-02,  1.2854e-02, -8.9608e-03,\n",
      "         -8.7589e-03, -4.0391e-02, -7.7729e-02, -4.5787e-03, -6.8576e-02,\n",
      "         -3.7583e-02, -8.9193e-02, -1.8642e-01, -8.3422e-02, -4.8964e-02,\n",
      "         -8.2656e-03,  1.0932e-01,  1.1501e-01,  1.5724e-01,  1.2533e-02,\n",
      "         -1.0276e-01, -1.3175e-01, -9.7643e-02, -1.1004e-01, -4.5918e-02,\n",
      "         -4.5686e-02,  3.1435e-02,  8.3693e-02,  5.6284e-02,  6.1224e-02,\n",
      "          1.0184e-02, -7.6625e-03, -1.9777e-02, -2.9795e-02,  4.7638e-02,\n",
      "          8.1597e-02,  5.2565e-02,  1.4546e-01,  1.6042e-01, -5.4418e-03,\n",
      "         -3.2704e-01, -7.3096e-02, -4.5389e-02, -3.0717e-02,  1.2020e-01,\n",
      "          2.5252e-01,  1.7481e-01,  3.8553e-02, -3.3330e-02, -3.6193e-02,\n",
      "         -8.5584e-02, -2.0912e-01, -8.3301e-02, -8.8698e-02,  2.0395e-02,\n",
      "          5.9135e-02,  8.5465e-02, -1.8893e-02, -7.2136e-02, -9.4409e-02,\n",
      "          4.0041e-03,  3.7570e-02,  6.8613e-02,  1.0187e-01,  1.0434e-01,\n",
      "          1.2005e-01,  1.7056e-01,  3.2934e-02, -3.5320e-01, -1.4199e-01,\n",
      "         -4.4031e-02, -1.2801e-02,  1.6470e-01,  2.2013e-01,  2.4455e-01,\n",
      "          7.7087e-02, -3.5167e-02, -3.0185e-02, -6.7617e-02, -1.0674e-01,\n",
      "         -2.9262e-01, -4.0786e-01, -3.3753e-01, -2.2127e-01, -1.2495e-01,\n",
      "         -1.3615e-01, -8.4794e-02, -1.0913e-04,  7.8572e-02,  2.8856e-02,\n",
      "          1.0497e-01,  2.7834e-02,  5.4645e-02,  1.8018e-01,  1.3764e-01,\n",
      "         -1.8537e-01, -4.0875e-01, -1.8560e-01, -5.4592e-02, -9.2937e-02,\n",
      "          6.6132e-02,  1.3484e-01,  3.5566e-01,  9.8767e-02,  4.2138e-02,\n",
      "          3.8418e-02, -9.3568e-03, -6.0476e-02, -2.1927e-01, -2.9592e-01,\n",
      "         -3.9225e-01, -4.3093e-01, -3.7518e-01, -2.0626e-01, -9.6670e-03,\n",
      "          9.3849e-02,  7.1248e-02,  7.2279e-02,  6.3078e-02,  5.8086e-02,\n",
      "          1.0365e-01,  1.6183e-01,  4.1330e-02, -3.1419e-01, -3.5512e-01,\n",
      "         -1.2015e-01, -5.7811e-03,  5.0989e-03,  1.9642e-02,  7.3201e-03,\n",
      "          3.0562e-01,  9.6509e-02,  1.0262e-01,  4.1101e-03, -1.8195e-02,\n",
      "          4.3700e-02, -5.7234e-02,  1.5568e-02, -1.1951e-02, -5.8464e-02,\n",
      "         -1.3246e-01, -1.5866e-01, -7.7490e-02,  2.3747e-02,  3.9429e-02,\n",
      "          4.6169e-02,  1.7187e-02,  6.0167e-03,  6.8719e-02,  9.8908e-03,\n",
      "         -3.1013e-02, -2.8894e-01, -2.5513e-01, -7.8784e-02, -4.0435e-02,\n",
      "          1.2023e-02,  5.5262e-02, -2.7326e-02,  3.0668e-01,  1.4546e-01,\n",
      "          7.9079e-02,  7.1483e-02,  3.7633e-02,  5.3798e-02,  3.6883e-02,\n",
      "          1.4158e-02,  2.2591e-02,  6.5785e-03, -2.4320e-02, -2.5703e-02,\n",
      "         -4.1692e-02, -2.2032e-02,  1.0335e-02,  6.5288e-02,  1.2867e-02,\n",
      "          3.3874e-02, -8.6114e-03, -1.0704e-01, -1.0175e-01, -2.5285e-01,\n",
      "         -2.0079e-01, -1.8792e-02,  2.8652e-02, -5.2337e-03,  4.6872e-02,\n",
      "          1.2506e-01,  1.7735e-01,  1.3436e-01,  7.9555e-02,  1.8387e-02,\n",
      "         -8.0773e-03, -6.9650e-05, -1.9808e-02, -3.2003e-02, -3.4457e-02,\n",
      "         -2.1352e-02, -1.2465e-02, -3.0364e-02, -4.0679e-02,  7.8995e-03,\n",
      "         -2.6077e-03,  6.4215e-02,  7.2420e-02,  3.5401e-02, -3.2259e-02,\n",
      "         -1.0059e-01, -1.4878e-01, -2.6796e-01, -1.3559e-01, -4.7577e-02,\n",
      "         -7.5424e-04, -4.2251e-04, -7.8264e-03,  1.4576e-01,  1.1656e-01,\n",
      "          1.0599e-01, -8.9660e-03,  5.4435e-02,  3.8377e-02, -1.6836e-02,\n",
      "         -9.8124e-03,  5.1525e-02,  5.7930e-02, -7.0933e-03,  1.6240e-02,\n",
      "         -1.0207e-04, -2.1737e-02,  1.7083e-03,  3.7649e-04, -2.6691e-02,\n",
      "          5.2196e-02,  2.7910e-02,  1.1427e-02,  2.9543e-03, -1.4918e-01,\n",
      "         -2.7742e-01, -1.9430e-01,  1.4095e-02, -1.7353e-02, -1.7331e-02,\n",
      "         -2.2234e-02,  6.1788e-02,  2.0497e-01,  1.9378e-01,  1.6579e-01,\n",
      "          1.3679e-01,  1.0374e-01,  4.8975e-02,  7.1490e-02,  2.7270e-02,\n",
      "          6.9535e-02,  2.0159e-02,  4.7805e-02, -9.3833e-03,  3.3598e-03,\n",
      "         -6.2364e-02, -1.7496e-03,  9.9905e-02,  7.8711e-02, -1.6128e-02,\n",
      "         -3.1686e-02, -2.4381e-01, -2.2266e-01,  1.0510e-03, -2.0153e-02,\n",
      "          1.0056e-01,  3.1560e-02, -1.4934e-03,  2.4821e-02, -3.2299e-02,\n",
      "          1.4481e-01,  2.2186e-01,  1.5792e-01,  1.9231e-01,  1.7137e-01,\n",
      "          1.1638e-01,  5.6882e-02,  8.3211e-02,  6.6130e-02,  2.2785e-02,\n",
      "          5.7218e-02,  2.0328e-02,  1.3722e-02,  2.7195e-02,  4.4180e-03,\n",
      "         -8.4157e-02, -1.1785e-01, -1.2732e-01, -9.4631e-02, -8.1269e-02,\n",
      "         -2.1700e-01, -4.4293e-02, -2.1884e-02,  7.8535e-03, -6.1293e-03,\n",
      "          2.5413e-02,  3.3348e-02,  5.4885e-03, -1.1759e-01,  4.5109e-02,\n",
      "          1.0344e-01,  7.1809e-02,  1.2100e-01,  8.2246e-02,  7.6992e-02,\n",
      "          1.0592e-01,  6.0443e-02,  1.0829e-01,  9.4906e-02,  6.4577e-02,\n",
      "          1.1339e-02, -5.3783e-03, -1.3435e-01, -1.1220e-01,  4.6237e-03,\n",
      "         -9.0395e-02, -1.3422e-01, -4.5649e-02, -1.1171e-01, -2.9540e-02,\n",
      "         -6.4824e-03, -2.7617e-02,  1.1230e-02,  2.7311e-02, -3.4675e-02,\n",
      "         -1.7609e-02,  1.7765e-03, -7.9896e-02, -1.5909e-01, -2.4082e-01,\n",
      "          1.1370e-02, -1.3926e-01, -2.1328e-01, -2.5059e-01, -2.8570e-01,\n",
      "         -2.8210e-01, -2.6710e-01, -3.8397e-01, -3.5178e-01, -2.2767e-01,\n",
      "         -1.6570e-01, -5.9229e-02,  7.1113e-02,  4.1966e-03,  3.0311e-02,\n",
      "          6.0831e-02, -1.3258e-02,  7.6518e-03, -6.4132e-04,  2.5310e-02,\n",
      "          2.5327e-02,  1.7977e-02,  3.4349e-02,  2.9186e-02,  7.0880e-03,\n",
      "          1.0696e-03,  2.2818e-03,  3.2812e-02,  3.1508e-02,  3.3935e-02,\n",
      "         -1.6946e-02, -5.3907e-02, -1.2520e-02, -9.5210e-02, -6.6394e-02,\n",
      "         -3.6783e-02, -6.0057e-02, -9.1797e-02, -1.9637e-03,  5.2775e-04,\n",
      "         -8.4753e-04, -2.6167e-02,  1.3307e-02,  1.4512e-02, -5.6130e-03,\n",
      "          1.0473e-02,  3.4695e-03, -3.4679e-02,  2.7112e-02],\n",
      "        [-1.7822e-02,  1.4845e-02, -1.9223e-02, -2.3511e-02, -1.2683e-02,\n",
      "         -6.5839e-03,  1.7703e-02,  4.9858e-03, -2.9964e-02,  1.8236e-02,\n",
      "          4.8061e-03, -2.3806e-03, -4.6265e-02, -5.1836e-02, -2.0448e-02,\n",
      "          1.3571e-02, -4.0719e-03,  3.0798e-02, -1.3028e-02,  2.6389e-02,\n",
      "         -3.2263e-03, -3.5098e-02,  2.8281e-02,  2.8241e-02, -7.8010e-03,\n",
      "          3.4198e-02, -1.3326e-03,  3.4970e-02, -2.4957e-02, -1.0536e-02,\n",
      "         -3.0047e-02, -2.5584e-02, -1.5305e-02, -4.2443e-02, -2.1070e-01,\n",
      "         -2.6338e-01, -2.0317e-01, -1.8142e-01, -2.7979e-01, -2.3144e-01,\n",
      "         -2.8173e-01, -2.5450e-01, -1.3174e-01, -2.4351e-01, -3.1278e-01,\n",
      "         -2.3541e-01, -2.1308e-01, -2.6388e-01, -2.7247e-01, -1.3624e-01,\n",
      "         -1.4603e-01, -1.3534e-01,  3.2452e-02, -1.8391e-02,  3.1968e-02,\n",
      "         -9.7839e-03,  1.5007e-02, -8.3181e-03, -1.0048e-02, -7.8670e-02,\n",
      "         -1.5413e-01, -3.7668e-02, -2.3255e-01, -3.5924e-01, -3.8814e-01,\n",
      "         -3.9710e-01, -4.9818e-01, -5.2930e-01, -4.9998e-01, -4.6359e-01,\n",
      "         -4.1490e-01, -4.7522e-01, -4.0870e-01, -2.6997e-01, -3.7773e-01,\n",
      "         -2.7573e-01, -2.0938e-01, -2.1989e-01, -2.4809e-01, -3.1928e-01,\n",
      "         -2.0864e-01, -1.2164e-01, -3.1971e-02, -1.1756e-02,  6.8946e-04,\n",
      "         -2.7751e-02, -3.5392e-02, -1.4399e-01, -9.7047e-02, -7.2847e-02,\n",
      "         -2.7782e-01, -3.7992e-01, -4.1817e-01, -5.1569e-01, -4.1155e-01,\n",
      "         -4.0147e-01, -5.1255e-01, -7.2508e-01, -5.2965e-01, -5.0232e-01,\n",
      "         -3.7996e-01, -3.0132e-01, -3.1211e-01, -1.4928e-01, -2.1688e-01,\n",
      "         -1.1973e-01, -1.2350e-01, -1.1671e-01, -2.0179e-01, -9.5542e-02,\n",
      "         -4.3293e-02,  7.7276e-03, -1.0996e-02,  1.6592e-02, -1.1509e-02,\n",
      "         -4.2472e-02,  2.3015e-02,  1.3885e-02, -2.0213e-01, -1.7928e-01,\n",
      "         -2.1702e-01, -1.8311e-01, -2.4760e-01, -1.9317e-01, -1.6171e-01,\n",
      "         -1.2612e-01, -2.1168e-01, -1.9673e-01, -9.8443e-02, -1.1428e-01,\n",
      "         -1.9745e-01, -3.0282e-02,  5.5304e-02,  5.0961e-02,  1.2138e-01,\n",
      "          1.4150e-01,  1.5913e-01, -7.2043e-02, -2.7562e-02, -1.3780e-02,\n",
      "         -3.3250e-02, -1.1394e-02, -1.1790e-01,  4.8650e-02,  1.1351e-01,\n",
      "          6.6478e-02,  1.1748e-02,  3.2690e-02, -4.4820e-02,  3.4940e-03,\n",
      "         -5.5205e-02, -7.1360e-02, -8.0630e-02, -1.0971e-01,  2.3123e-03,\n",
      "          1.4080e-02,  1.1699e-01,  7.2051e-02,  1.0063e-01,  7.5343e-02,\n",
      "          7.7005e-02,  6.7358e-02,  8.8808e-02,  1.5743e-01,  1.8864e-02,\n",
      "         -1.1563e-01, -2.3239e-01, -9.5762e-03, -2.2906e-02, -2.6102e-02,\n",
      "         -3.6336e-02, -1.2550e-01,  5.8315e-02,  1.3700e-01,  7.3740e-02,\n",
      "          9.3692e-02,  5.0719e-03,  3.3775e-02, -5.2583e-02, -9.4181e-02,\n",
      "         -1.3295e-01, -1.9680e-01, -1.9182e-01, -2.2949e-01, -2.1852e-01,\n",
      "         -1.5616e-01, -1.1814e-01, -3.8401e-02, -2.9992e-02, -3.1961e-02,\n",
      "          1.7481e-01,  1.9921e-01, -1.2745e-02, -8.6122e-02,  8.6592e-02,\n",
      "         -5.9054e-02,  2.7848e-02, -1.4701e-02, -6.1263e-02, -3.3943e-03,\n",
      "          4.4259e-02,  9.3816e-02, -5.3896e-03,  3.4167e-02,  1.0805e-03,\n",
      "         -2.9214e-02, -3.0578e-02, -7.4972e-02, -1.8074e-01, -1.6445e-01,\n",
      "         -2.6028e-01, -2.7045e-01, -2.1233e-01, -1.4676e-01, -7.2872e-02,\n",
      "         -4.3925e-02, -1.5592e-02,  3.2503e-02,  1.1536e-01,  7.4959e-02,\n",
      "         -3.5284e-02, -1.2214e-01,  7.1845e-03, -1.0634e-01, -4.6522e-02,\n",
      "         -9.6868e-02,  8.0045e-03, -1.1008e-02,  3.9484e-02,  4.0589e-02,\n",
      "         -3.5206e-02,  4.2037e-02, -4.3454e-02,  8.6130e-05, -5.3635e-02,\n",
      "         -8.6846e-02, -1.4986e-01, -1.4580e-01, -2.2385e-01, -2.3572e-01,\n",
      "         -2.0273e-01, -7.8194e-02, -4.0325e-02, -2.7583e-02, -1.5078e-02,\n",
      "         -3.8430e-02,  1.5329e-02,  3.7251e-02, -1.4553e-02, -6.5061e-02,\n",
      "         -1.2328e-01, -1.2458e-01, -3.0933e-02, -9.9751e-02,  1.1372e-02,\n",
      "         -2.2303e-01, -2.2815e-02,  3.2096e-02, -2.7022e-02, -2.3591e-02,\n",
      "         -1.2500e-02, -4.9095e-02, -6.0382e-02, -7.4754e-02, -6.7353e-02,\n",
      "         -9.2561e-02, -2.7449e-01, -2.4063e-01, -3.5226e-02, -3.7856e-02,\n",
      "         -1.5109e-02,  2.0160e-02,  9.5251e-03,  2.3674e-03,  3.0577e-02,\n",
      "         -5.8275e-02, -1.5851e-01, -1.3847e-01, -2.0572e-01, -7.5557e-02,\n",
      "         -2.5578e-02, -7.8696e-02, -1.1443e-01, -2.3368e-01, -5.3777e-02,\n",
      "         -9.7224e-02, -1.2148e-01, -1.8224e-02, -1.4006e-02,  4.3557e-02,\n",
      "         -3.5640e-02, -3.4033e-02,  1.8836e-02, -1.2109e-01, -3.8315e-01,\n",
      "         -6.0434e-02,  2.2007e-02, -1.1315e-02, -2.7905e-02, -1.0797e-02,\n",
      "         -1.5107e-02, -1.3647e-02,  3.3739e-02, -8.9104e-02, -8.1749e-02,\n",
      "         -1.4191e-01, -2.3385e-01, -5.4801e-03, -1.3140e-02, -9.5238e-02,\n",
      "         -2.3629e-01, -1.5850e-01, -6.8084e-02, -3.9965e-02, -6.4410e-02,\n",
      "         -3.4404e-02,  2.6522e-02,  2.9429e-02,  8.9577e-02,  8.1184e-02,\n",
      "          1.0012e-01, -2.3534e-01, -3.9013e-01,  3.9256e-02,  1.4248e-01,\n",
      "          4.8600e-02,  5.4920e-04,  2.3811e-02, -7.3669e-02, -1.0317e-01,\n",
      "         -8.8573e-02, -2.0004e-01, -2.7704e-01, -1.7847e-01, -1.1246e-01,\n",
      "         -1.2150e-01, -6.8046e-03, -7.9488e-02, -1.6846e-01, -1.6814e-01,\n",
      "         -1.0861e-01, -2.2972e-02,  4.1767e-02,  8.8499e-02,  5.2410e-02,\n",
      "          1.0348e-01,  1.6789e-01,  2.6839e-01,  3.0124e-01, -1.0502e-01,\n",
      "         -1.6118e-01,  1.3151e-01,  1.2746e-01,  2.6822e-02, -1.8823e-02,\n",
      "         -3.8687e-02, -5.5261e-02,  1.9816e-02, -4.2136e-02, -2.4213e-01,\n",
      "         -1.2066e-01, -1.4037e-01, -1.5157e-01, -1.2028e-01,  2.6431e-02,\n",
      "          8.4375e-03, -1.6043e-01, -1.9379e-01, -6.7517e-02,  1.6822e-02,\n",
      "          1.0742e-01,  1.1763e-01,  1.4038e-01,  1.3510e-01,  2.1416e-01,\n",
      "          3.4355e-01,  1.6057e-01, -1.2352e-01, -8.9299e-02,  9.2642e-02,\n",
      "          1.1190e-01,  1.4931e-01,  3.1672e-02,  1.5382e-02,  6.3861e-02,\n",
      "          7.4560e-02, -1.3711e-04, -1.8735e-01, -1.2636e-01, -1.0180e-01,\n",
      "         -2.4245e-01, -9.0738e-02, -1.0288e-02,  7.9920e-03, -1.3189e-01,\n",
      "         -5.2255e-02,  1.9487e-01,  1.3892e-01,  1.6863e-01,  1.4355e-01,\n",
      "          1.1485e-01,  1.1484e-01,  1.6048e-01,  2.0484e-01,  9.0562e-02,\n",
      "         -8.3317e-02, -2.0333e-02,  6.1859e-02,  1.7141e-01,  2.0350e-01,\n",
      "          9.1707e-02,  1.5199e-01,  8.3227e-02,  2.9852e-02, -9.4499e-03,\n",
      "         -1.1896e-01, -9.7102e-02, -1.0127e-01, -2.7612e-01, -9.3755e-02,\n",
      "          2.8974e-02,  4.2154e-02,  1.1332e-01,  3.2257e-02,  1.0297e-01,\n",
      "          7.7260e-02,  8.8483e-02,  1.3096e-01,  1.4924e-01,  1.4200e-01,\n",
      "          1.1463e-01,  1.0541e-01,  7.7661e-02, -1.2258e-03,  5.6931e-02,\n",
      "          1.4264e-01,  2.0640e-01,  1.6380e-01,  3.0126e-02,  9.6830e-02,\n",
      "          1.5914e-02, -6.1604e-02,  4.1742e-02, -3.0123e-02, -2.5223e-01,\n",
      "         -1.8873e-01, -2.0599e-01,  2.4092e-02, -6.1235e-03, -4.8376e-03,\n",
      "         -4.4017e-02, -7.1502e-02, -1.8073e-01,  1.6670e-02,  6.5972e-02,\n",
      "          9.2490e-02,  1.6237e-01,  8.7371e-02,  2.9262e-02,  1.1815e-02,\n",
      "          4.7140e-02,  4.9001e-02,  1.3248e-01,  1.9528e-01,  1.9714e-01,\n",
      "          1.2038e-01,  1.3093e-01,  6.6290e-02,  1.0759e-01,  8.4532e-02,\n",
      "          4.0054e-02, -5.0588e-02, -1.9760e-01, -2.9906e-01, -2.4987e-01,\n",
      "         -8.2348e-03,  6.2497e-03,  1.3005e-02,  5.4359e-02, -2.7540e-01,\n",
      "         -1.4812e-01, -2.8333e-02,  5.2290e-02,  1.1660e-01,  1.1568e-01,\n",
      "          6.9671e-02, -3.6144e-02, -5.2030e-02, -1.1186e-02,  1.6942e-01,\n",
      "          1.9769e-01,  1.7442e-01,  1.5968e-01,  8.1311e-02,  7.7399e-02,\n",
      "          4.1658e-02,  2.1439e-02,  4.2653e-02, -3.1847e-02, -5.0560e-02,\n",
      "         -2.3973e-01, -3.3165e-01, -1.5163e-01, -1.2794e-01, -6.7875e-02,\n",
      "          3.2625e-03,  3.7004e-02, -1.5222e-01, -1.4987e-01,  3.9394e-03,\n",
      "          4.5595e-02,  5.8446e-02,  6.6046e-02,  2.9288e-02, -1.3122e-01,\n",
      "         -7.8958e-02,  8.3620e-02,  1.6033e-01,  1.5933e-01,  7.6815e-02,\n",
      "          2.7176e-02, -6.2961e-02, -8.9097e-02, -8.0317e-02, -1.9476e-02,\n",
      "         -3.6445e-02, -1.8773e-01, -1.6072e-01, -2.3889e-01, -2.7222e-01,\n",
      "         -5.9316e-02,  6.7669e-02, -1.1953e-02, -1.2314e-01, -3.5233e-02,\n",
      "         -3.7550e-02, -8.1191e-02, -2.6935e-02, -7.4958e-02, -1.3244e-01,\n",
      "         -1.8793e-01, -1.8897e-01, -2.4068e-01, -1.9810e-01, -6.3655e-02,\n",
      "         -1.0492e-03,  5.4571e-02, -2.6020e-03, -3.8853e-02, -8.7382e-02,\n",
      "         -6.9652e-02, -1.1887e-01, -1.2737e-01, -8.7993e-02, -3.1065e-01,\n",
      "         -3.0431e-01, -2.8321e-01, -2.9451e-01, -5.9072e-03, -1.1359e-02,\n",
      "          1.9572e-02, -2.8748e-02,  5.7760e-02, -2.5140e-04, -1.7447e-01,\n",
      "         -1.6132e-01, -1.6260e-01, -3.0131e-01, -2.9771e-01, -1.8881e-01,\n",
      "         -1.8879e-01, -1.0299e-01, -1.3803e-01, -1.5827e-01, -6.3253e-02,\n",
      "         -2.7050e-02, -1.0450e-02, -1.1740e-02, -3.4222e-02, -1.2178e-01,\n",
      "         -9.3348e-02, -5.1700e-02, -1.3122e-01, -1.5040e-01, -1.7647e-01,\n",
      "         -1.2762e-01, -2.0163e-03, -1.6412e-02,  4.9531e-03, -2.1570e-02,\n",
      "         -2.1438e-02, -1.9065e-01, -1.4991e-01, -1.4928e-01, -2.6390e-01,\n",
      "         -2.9317e-01, -1.2113e-01, -6.0105e-02, -1.6543e-02, -6.5394e-02,\n",
      "         -9.8143e-02, -8.8698e-02, -5.0412e-02, -1.4593e-02, -4.8643e-02,\n",
      "          3.6023e-02, -2.6548e-02, -2.4622e-02, -5.2261e-02, -1.3561e-02,\n",
      "         -2.8140e-03, -1.8768e-02, -1.9343e-02, -7.0191e-02, -5.3940e-03,\n",
      "          5.0276e-03,  3.1016e-02, -2.1104e-03, -5.5746e-02, -2.0030e-01,\n",
      "         -2.3778e-01, -1.6612e-01, -2.0347e-01, -1.4455e-01, -5.5199e-02,\n",
      "         -7.7808e-02, -5.8675e-02, -7.5064e-02, -5.1188e-02, -6.9356e-02,\n",
      "         -1.1046e-01, -3.0032e-03, -2.3626e-02,  1.9858e-02,  4.5461e-02,\n",
      "          9.1606e-02,  3.1897e-02,  1.0187e-01, -1.4211e-02,  2.5889e-02,\n",
      "          3.0634e-02, -5.2127e-02,  9.2174e-02,  1.9127e-02, -2.2300e-02,\n",
      "         -2.4167e-03, -5.0114e-02, -1.5258e-01, -2.1006e-01, -1.0188e-01,\n",
      "         -1.4516e-02, -3.4533e-02, -1.5296e-02, -5.2952e-02, -8.9326e-02,\n",
      "         -4.1469e-02, -7.5139e-02, -5.6720e-02, -3.7931e-03, -1.5745e-02,\n",
      "          1.1257e-02,  2.0983e-02,  5.9066e-02,  3.4005e-02,  1.0219e-01,\n",
      "          1.0746e-01, -3.5695e-02, -5.0198e-02, -1.2238e-01,  4.4030e-02,\n",
      "          9.3692e-02, -1.5989e-02, -3.3545e-02, -2.6890e-02, -3.4761e-02,\n",
      "         -9.9831e-02, -1.4657e-01,  8.3102e-02,  3.3059e-02, -6.8414e-02,\n",
      "         -4.1694e-02, -1.6351e-02, -4.0050e-02,  2.2405e-02, -7.0456e-02,\n",
      "         -8.5734e-02, -5.4963e-02, -7.7871e-02, -4.3844e-02, -6.5006e-02,\n",
      "         -5.9737e-02, -2.5255e-02, -6.5175e-02, -6.1946e-02, -5.3567e-02,\n",
      "         -6.4155e-02, -1.0002e-01,  1.2413e-01,  1.8318e-03,  9.4252e-03,\n",
      "          1.0994e-02, -2.8661e-02, -1.1764e-02, -1.6410e-02, -1.4364e-01,\n",
      "         -1.9875e-01, -2.0253e-01, -2.2186e-01, -2.2446e-01, -2.1586e-01,\n",
      "         -1.5009e-01, -2.1393e-01, -2.0627e-01, -1.6253e-01, -1.7944e-01,\n",
      "         -1.8012e-01, -2.8236e-01, -3.0772e-01, -3.1093e-01, -3.6339e-01,\n",
      "         -3.7866e-01, -3.9692e-01, -2.4369e-01, -5.7363e-02, -8.6564e-02,\n",
      "         -1.6269e-02,  1.5381e-02, -2.5652e-03,  1.3050e-02, -2.4410e-02,\n",
      "          5.3483e-02,  2.0331e-02, -3.5237e-02, -1.8329e-01, -2.5852e-01,\n",
      "         -4.2744e-01, -3.4178e-01, -3.4708e-01, -3.2304e-01, -4.2180e-01,\n",
      "         -3.4865e-01, -3.5338e-01, -4.6275e-01, -3.6090e-01, -4.2027e-01,\n",
      "         -4.9433e-01, -4.8340e-01, -3.3189e-01, -3.7145e-01, -3.3212e-01,\n",
      "         -2.3479e-01, -9.1686e-02,  1.2889e-02,  1.7301e-02,  2.5235e-03,\n",
      "         -2.2398e-02, -5.2345e-03,  1.0686e-02,  3.1370e-02, -2.7210e-02,\n",
      "          3.3317e-02,  1.0860e-03, -7.9875e-02, -1.3733e-01, -1.9110e-01,\n",
      "         -1.4079e-01, -1.8862e-01, -1.3661e-01, -1.4114e-01, -2.0174e-01,\n",
      "         -2.8582e-01, -1.8192e-01, -7.9626e-02, -1.3490e-01, -2.0958e-01,\n",
      "         -1.6906e-01, -8.8361e-02, -1.2314e-01, -7.0977e-02, -2.7361e-02,\n",
      "          5.7260e-03,  2.8205e-02,  1.7490e-02,  1.7777e-02],\n",
      "        [ 6.8063e-03,  3.1360e-02, -2.4028e-02, -3.0303e-03,  2.9774e-03,\n",
      "          9.4479e-03, -3.0195e-02,  8.3407e-04, -5.5938e-02, -2.0632e-02,\n",
      "          1.7875e-02, -3.4064e-02,  1.4408e-02,  1.5747e-02, -2.8424e-02,\n",
      "         -2.8611e-02, -1.4161e-03, -8.1038e-03, -2.9927e-02,  2.5029e-02,\n",
      "          1.0551e-02, -3.5008e-03,  1.7449e-02,  6.7604e-03,  6.1507e-03,\n",
      "          2.7305e-02, -2.4011e-02,  7.2094e-03,  2.5286e-02,  1.3795e-02,\n",
      "         -5.0211e-03, -2.2473e-02, -1.9669e-02,  1.0938e-02, -3.3036e-02,\n",
      "          3.2460e-02, -3.2093e-02, -5.7623e-02, -4.1591e-02, -5.1024e-02,\n",
      "         -9.1683e-02, -3.2033e-02, -1.5079e-02, -2.2441e-02,  2.2232e-02,\n",
      "          3.1453e-02, -1.4420e-02, -5.9418e-02, -1.3598e-02, -2.1759e-02,\n",
      "          3.7827e-03,  9.6109e-03, -2.5949e-03,  3.5511e-02, -1.5140e-02,\n",
      "         -3.0513e-02, -3.4330e-02,  2.8061e-02, -3.3124e-02, -8.4062e-02,\n",
      "         -2.3824e-03, -7.0091e-03,  7.8319e-03, -3.2728e-02, -3.0816e-02,\n",
      "         -5.1501e-02, -1.2946e-01, -1.4179e-01, -1.9503e-01, -2.7426e-01,\n",
      "         -3.8118e-01, -3.5224e-01, -1.1190e-01, -1.1886e-01, -9.3850e-02,\n",
      "         -3.2142e-02, -1.6794e-01, -2.3295e-01, -1.4921e-01, -1.9255e-01,\n",
      "         -1.0540e-01, -3.1132e-02, -2.1808e-02, -6.8659e-03, -1.8648e-02,\n",
      "          1.3077e-02, -3.5640e-03, -1.0568e-01, -3.0331e-02, -1.3608e-01,\n",
      "         -2.2885e-01, -1.9214e-01, -6.6739e-02, -1.7545e-01, -9.6189e-02,\n",
      "         -1.9310e-01, -4.1013e-02, -7.1547e-02, -6.1827e-02, -9.3093e-02,\n",
      "         -1.5189e-01, -1.0365e-01, -4.0786e-02, -5.6378e-02, -5.4698e-02,\n",
      "          2.8697e-03, -1.0755e-02, -2.2536e-01, -1.4812e-01,  2.7865e-02,\n",
      "          6.8337e-02,  5.6072e-03,  3.0648e-02,  1.2128e-02,  2.1870e-02,\n",
      "         -2.7521e-02, -2.0123e-01, -2.5453e-01, -1.4706e-01, -1.2185e-01,\n",
      "         -2.2075e-01, -1.0353e-01, -7.6191e-02, -2.6726e-02, -2.4054e-02,\n",
      "         -1.0763e-01, -3.8492e-02,  5.9548e-02,  6.3035e-02,  4.3170e-02,\n",
      "          3.7532e-02, -4.3644e-02, -2.3992e-02,  1.1463e-01,  8.1114e-02,\n",
      "          1.2625e-01,  1.4424e-01,  8.4696e-02, -3.5420e-02, -3.2901e-02,\n",
      "          1.1385e-02,  1.4835e-02, -2.7638e-02, -1.0450e-01, -3.1672e-01,\n",
      "         -2.7875e-01, -2.2911e-01, -1.2594e-01, -8.7295e-02, -6.7897e-02,\n",
      "         -2.2998e-02,  2.5913e-02, -4.8588e-02, -5.0547e-02, -5.0859e-02,\n",
      "         -1.6432e-02,  1.2218e-02,  8.2853e-02,  3.0867e-02,  4.5971e-02,\n",
      "          9.2056e-02,  1.2079e-01,  1.4163e-01,  1.0360e-01,  1.2879e-01,\n",
      "          1.4256e-01,  1.0730e-01,  1.0255e-01,  2.9957e-02, -3.9531e-02,\n",
      "         -8.8120e-03, -2.2503e-01, -2.6630e-01, -1.3939e-01, -1.2994e-01,\n",
      "         -8.9105e-02, -4.7643e-02, -2.3617e-02, -8.6641e-03,  1.9795e-02,\n",
      "         -2.2703e-02,  2.3732e-02,  3.8937e-02, -4.2136e-02, -2.5629e-02,\n",
      "          8.9944e-03,  5.4692e-03,  5.7530e-02,  6.5673e-02,  6.1746e-03,\n",
      "          1.1819e-01,  1.8518e-01,  2.4735e-01,  2.3419e-01,  1.7226e-01,\n",
      "          1.7493e-01, -2.0348e-02, -9.5040e-03, -8.8382e-03, -1.9994e-01,\n",
      "         -3.7032e-01, -2.1547e-01, -1.1918e-01, -8.3396e-02,  1.8234e-02,\n",
      "          6.9237e-03,  6.4462e-02,  4.3571e-02,  2.2517e-02, -7.6767e-04,\n",
      "         -6.9860e-03, -7.3789e-02,  3.6109e-03,  2.7473e-02,  1.3628e-02,\n",
      "          2.0645e-02,  6.4436e-02,  1.1145e-01,  8.0918e-02,  1.7421e-01,\n",
      "          3.5202e-01,  3.4431e-01,  1.2045e-01,  1.3810e-01, -1.5562e-02,\n",
      "         -1.5757e-02, -6.1194e-02, -2.3830e-01, -4.4999e-01, -1.8133e-01,\n",
      "         -1.5372e-01, -6.0418e-02,  5.9197e-02,  3.6167e-04,  3.5495e-02,\n",
      "          2.9059e-02,  4.0583e-03, -3.4487e-02, -6.8795e-02, -6.6870e-02,\n",
      "         -7.3173e-02,  1.3732e-03,  7.4706e-03,  2.6597e-02,  7.9546e-02,\n",
      "          1.2514e-01,  1.1651e-01,  2.3528e-01,  4.4295e-01,  4.6453e-01,\n",
      "          2.7276e-01,  4.1684e-02, -8.6104e-03, -1.1245e-02, -1.0534e-01,\n",
      "         -2.1856e-01, -2.4117e-01, -1.2903e-01, -4.6209e-02, -1.4650e-02,\n",
      "          4.3356e-02,  8.6962e-02,  6.3009e-02,  1.4742e-01,  1.0102e-01,\n",
      "         -1.6171e-03, -1.0276e-01, -1.7564e-01, -1.5861e-01, -1.1958e-01,\n",
      "         -6.4862e-02, -3.7446e-02,  2.7464e-02,  1.5957e-01,  2.5885e-01,\n",
      "          4.8586e-01,  7.9690e-01,  8.8797e-01,  4.4337e-01,  2.2339e-01,\n",
      "          3.2529e-02,  7.0629e-03, -2.3422e-02, -2.9437e-01, -6.4166e-02,\n",
      "         -9.1658e-02, -3.1909e-02,  8.6633e-02,  1.3130e-01,  1.5188e-01,\n",
      "          1.4593e-01,  1.9701e-01,  2.0061e-01,  1.1778e-01,  1.4186e-02,\n",
      "         -1.4388e-01, -1.8191e-01, -1.8910e-01, -2.3130e-01, -2.8806e-01,\n",
      "         -2.7263e-01, -1.5429e-01, -4.9541e-02,  1.9030e-01,  5.5038e-01,\n",
      "          8.5543e-01,  5.1066e-01,  1.2693e-01, -1.8568e-02, -3.2388e-02,\n",
      "         -3.0893e-02, -3.3995e-01, -8.8201e-02, -3.2006e-02,  1.5107e-01,\n",
      "          4.7880e-02,  7.2994e-02,  7.8710e-02,  1.0912e-01,  1.1577e-01,\n",
      "          1.5206e-01,  1.6983e-01,  1.7859e-02, -9.5128e-02, -1.1588e-01,\n",
      "         -1.4232e-01, -2.0210e-01, -4.3350e-01, -7.7246e-01, -1.0179e+00,\n",
      "         -1.0234e+00, -8.5550e-01, -5.4352e-01,  3.3082e-01,  3.5934e-01,\n",
      "          6.5762e-02,  3.1169e-02, -8.8346e-02, -9.1229e-02, -1.1417e-01,\n",
      "         -5.7860e-02,  8.7717e-02,  1.5877e-01,  5.9338e-02,  5.0914e-02,\n",
      "          2.2771e-03,  6.5743e-02,  1.5808e-01,  1.2950e-01,  1.0444e-01,\n",
      "          8.9288e-03, -8.9167e-02, -1.3971e-01, -1.0559e-01, -1.1393e-01,\n",
      "         -6.8866e-02, -2.0772e-01, -5.4149e-01, -9.7400e-01, -1.1163e+00,\n",
      "         -9.7983e-01, -4.3701e-01,  1.2756e-01, -4.2958e-02,  1.0615e-02,\n",
      "         -1.0978e-01, -9.0737e-02, -2.6824e-02,  2.5865e-02,  1.4171e-01,\n",
      "          5.8926e-02,  8.6663e-02, -3.4410e-03,  1.1619e-02,  1.2390e-01,\n",
      "          1.5354e-01,  1.0466e-01,  6.8745e-02, -5.9410e-03, -9.7647e-02,\n",
      "         -1.4998e-01, -1.5691e-01, -3.0642e-02,  7.4227e-02,  1.1475e-01,\n",
      "          1.0772e-02, -2.5300e-01, -7.2642e-01, -8.0384e-01, -4.3000e-01,\n",
      "         -9.8096e-02,  1.7779e-02,  1.3556e-01, -3.7551e-02,  3.7488e-03,\n",
      "         -1.2728e-01,  4.5080e-02,  1.6712e-02, -3.9092e-02, -5.5918e-03,\n",
      "         -2.3376e-02,  9.4626e-02,  1.3741e-01,  6.3577e-02,  7.2890e-02,\n",
      "          2.1509e-02, -1.2528e-02, -5.1387e-02, -5.1631e-02, -8.0714e-02,\n",
      "         -4.7078e-02, -4.4763e-02,  5.8715e-02,  5.2253e-02,  3.5361e-02,\n",
      "         -1.1530e-01, -3.1600e-01, -3.7290e-01, -1.2431e-01,  2.3340e-02,\n",
      "          4.7561e-02, -2.2489e-02, -1.7030e-02, -1.4281e-01, -5.5192e-02,\n",
      "         -1.7867e-01, -1.8719e-01, -1.0238e-01,  1.0803e-02,  7.9254e-02,\n",
      "          7.6021e-02,  4.7662e-02,  5.2159e-02, -2.6268e-02, -7.7780e-02,\n",
      "         -1.2233e-01, -6.7765e-02, -6.2199e-02,  7.3695e-04, -1.7228e-02,\n",
      "         -1.2578e-02, -2.2738e-02, -2.3217e-02,  4.8634e-03, -5.1690e-02,\n",
      "         -1.6323e-01, -1.7145e-01, -3.3227e-02, -1.1672e-01, -1.8647e-02,\n",
      "         -1.0593e-02,  2.4813e-02,  7.8818e-03, -1.1168e-01, -2.1992e-01,\n",
      "         -2.0429e-01, -1.6894e-01, -4.6958e-02,  2.0010e-02,  3.0824e-02,\n",
      "          9.6184e-03, -7.1157e-03, -7.2493e-02, -6.3618e-02, -8.3829e-02,\n",
      "         -1.8065e-02,  1.1168e-02,  1.3017e-02, -1.4114e-03, -4.8263e-02,\n",
      "         -1.1518e-02,  4.7218e-02,  5.6681e-02, -1.0435e-01, -2.3711e-01,\n",
      "         -1.7247e-02,  3.1693e-02, -1.1156e-02, -1.6281e-01,  2.0806e-02,\n",
      "          7.5012e-02,  7.3599e-02,  1.1407e-01, -9.7133e-02, -2.5955e-01,\n",
      "         -2.2585e-01, -2.1707e-01, -1.5137e-01, -1.8904e-01, -1.7757e-01,\n",
      "         -1.2716e-01, -3.7237e-02,  3.7360e-03,  2.0037e-02,  4.6727e-02,\n",
      "          9.3512e-04,  1.8781e-02, -2.9223e-02, -2.7549e-02,  8.2748e-03,\n",
      "          8.1947e-02, -2.9877e-01, -2.0864e-01, -4.6883e-02,  1.4372e-02,\n",
      "         -4.1824e-02, -1.3217e-01, -5.2381e-02,  1.1900e-01,  2.9383e-02,\n",
      "          1.9943e-01,  4.6665e-02, -8.0262e-02, -2.4920e-01, -3.0940e-01,\n",
      "         -3.1224e-01, -1.6074e-01,  1.4687e-02,  2.5577e-02,  3.3206e-02,\n",
      "          4.6600e-02,  5.0116e-02, -1.7384e-02, -1.7949e-03,  5.8338e-02,\n",
      "          2.0281e-02,  1.4087e-02,  2.1362e-02,  9.7737e-02, -2.2917e-01,\n",
      "         -1.1443e-01, -1.1614e-01,  2.4903e-02, -6.8188e-02, -1.5988e-02,\n",
      "         -2.2245e-01,  5.9944e-02,  3.5439e-02,  1.0624e-01,  3.7618e-02,\n",
      "          8.6084e-02,  5.7531e-02, -1.6738e-02, -6.9394e-03,  8.1996e-02,\n",
      "          1.0724e-01,  8.5020e-02,  5.1305e-02,  2.5215e-02, -1.3020e-02,\n",
      "          1.0523e-02, -3.3948e-02, -4.0415e-02, -1.5329e-02,  5.3214e-02,\n",
      "          4.4583e-02,  1.1044e-01, -2.2516e-01, -1.6418e-01, -6.6408e-02,\n",
      "         -1.0493e-03, -5.2220e-02, -1.3980e-02, -1.4349e-01, -1.2014e-02,\n",
      "          2.5496e-02,  2.5506e-02,  5.4622e-02,  4.4309e-02,  7.6473e-02,\n",
      "          1.0200e-01,  1.7147e-01,  4.6925e-02,  3.9654e-02, -2.8880e-02,\n",
      "         -7.3770e-02,  3.3529e-02, -1.1404e-02,  2.9505e-02,  2.7980e-02,\n",
      "         -8.6082e-03,  2.9765e-02,  1.2074e-01,  1.0596e-01, -1.7222e-02,\n",
      "         -8.3091e-02, -7.8933e-02,  2.9475e-02, -1.9299e-02, -3.3387e-02,\n",
      "         -1.0830e-01, -7.1649e-02,  4.1602e-02, -9.6049e-03,  1.8251e-02,\n",
      "          6.1216e-03,  5.7925e-02,  6.0240e-02,  3.8689e-02,  4.5027e-02,\n",
      "          1.4101e-02, -1.7836e-02,  1.0877e-03,  8.6936e-03, -3.2196e-04,\n",
      "          2.6894e-02, -3.9524e-02,  2.2114e-02,  7.1279e-02,  1.1892e-01,\n",
      "          1.1772e-01,  1.5393e-01, -6.5044e-02, -1.0291e-01, -7.0413e-02,\n",
      "          2.6904e-02, -2.3632e-02, -2.9026e-02, -1.2808e-01, -2.4335e-01,\n",
      "          1.0882e-01, -1.6985e-02, -5.2278e-02, -4.5498e-02,  6.5065e-03,\n",
      "          4.9548e-02,  4.4169e-02,  7.9257e-02,  5.7058e-02,  8.4156e-02,\n",
      "          4.2820e-02,  2.3716e-02,  5.0606e-02, -1.5938e-02,  2.3244e-02,\n",
      "          6.0510e-02,  3.1457e-02,  5.9558e-02,  1.1626e-01,  1.1304e-01,\n",
      "         -3.8519e-02, -7.0058e-02,  6.0267e-02, -5.6938e-03,  5.6389e-03,\n",
      "         -1.7312e-02, -8.3096e-02, -1.1808e-01,  7.4259e-02,  1.5422e-01,\n",
      "         -9.2703e-03, -1.7123e-02, -7.0354e-03,  1.8735e-02,  6.5749e-02,\n",
      "          7.1121e-02,  8.6300e-02,  6.8825e-02,  8.5469e-02,  5.1228e-02,\n",
      "          9.3187e-03,  1.5532e-02, -5.1090e-02, -5.4769e-02,  1.9576e-02,\n",
      "          9.7950e-03,  3.8734e-02,  7.1844e-02, -4.1973e-04, -6.2755e-02,\n",
      "          1.3695e-01,  3.1533e-02, -1.4552e-02, -6.1575e-03, -8.7506e-02,\n",
      "         -3.3722e-02, -4.4954e-02, -9.3686e-04,  3.9225e-02,  3.8128e-02,\n",
      "         -3.8676e-02,  7.7828e-02, -5.6494e-03,  7.1347e-03,  3.7701e-02,\n",
      "          3.8025e-02,  9.2058e-02,  9.3357e-02,  2.3064e-02,  2.7114e-02,\n",
      "          8.1717e-02, -1.6552e-02,  6.5762e-03,  7.3177e-03,  1.0385e-01,\n",
      "          7.2787e-02,  9.4254e-02, -1.6165e-01, -1.2523e-01,  3.5482e-02,\n",
      "          4.1588e-03, -1.2165e-02,  3.2910e-02,  1.2135e-01,  1.0557e-01,\n",
      "         -7.4885e-03, -1.6507e-02, -1.3595e-02, -9.6040e-02,  6.8325e-02,\n",
      "          1.4297e-02,  7.1553e-02,  5.3208e-02,  1.3623e-01,  7.9808e-02,\n",
      "          1.1464e-01,  1.5657e-01,  1.1116e-01,  7.8953e-02,  2.1092e-03,\n",
      "         -5.2442e-02,  6.4639e-02,  1.9647e-01,  1.9427e-01,  1.9005e-01,\n",
      "          2.5553e-02,  7.8178e-03, -7.4259e-03, -7.7182e-04,  3.1437e-02,\n",
      "          5.4579e-03,  1.6792e-02,  1.2744e-02, -6.1163e-02, -1.9762e-01,\n",
      "         -3.0079e-01, -2.6617e-01, -1.1370e-01, -1.2116e-01,  3.5466e-02,\n",
      "          4.5719e-02, -2.1198e-02, -1.0968e-01, -5.7693e-03,  4.2998e-02,\n",
      "          4.5428e-02, -7.1087e-02, -1.0559e-01, -2.1932e-02, -9.2343e-03,\n",
      "          7.5133e-02,  9.6946e-03, -3.2498e-03, -1.3486e-02, -9.1094e-03,\n",
      "          6.9878e-04, -1.0557e-02, -3.1967e-02, -1.1100e-02,  2.6158e-02,\n",
      "         -9.1291e-03,  1.5652e-02, -8.8150e-03,  8.5045e-03, -2.2720e-02,\n",
      "         -3.6354e-02,  2.7430e-02,  2.0643e-02,  3.9889e-02, -5.7453e-02,\n",
      "         -1.5979e-01, -7.7883e-02, -2.7290e-02, -7.4942e-02, -3.1198e-02,\n",
      "         -3.2298e-02, -7.3897e-02, -1.0268e-01, -1.2995e-01, -8.6476e-03,\n",
      "          1.8944e-02,  3.3967e-02,  1.5163e-02, -8.7039e-03],\n",
      "        [ 2.9908e-02,  3.3683e-02, -1.7866e-02,  2.2986e-02, -1.0667e-02,\n",
      "         -9.8565e-03, -2.7261e-02,  2.8595e-02,  9.5555e-03,  2.0586e-02,\n",
      "         -3.4798e-02, -3.3115e-02,  8.3322e-02,  1.0946e-01, -1.5125e-02,\n",
      "          2.6772e-02,  2.7082e-02,  8.6092e-03,  2.1455e-02,  1.0339e-02,\n",
      "          2.8100e-02,  7.8575e-03, -2.9590e-03,  2.4076e-02,  1.1454e-03,\n",
      "         -2.2425e-02,  3.9020e-03, -3.5245e-02,  5.7742e-04,  1.7927e-02,\n",
      "         -1.4211e-02,  9.0081e-03,  2.3923e-02,  3.2822e-02,  4.5692e-02,\n",
      "          6.3197e-02,  8.7787e-02,  3.4886e-02,  4.9225e-02,  1.0414e-01,\n",
      "          1.4169e-01,  1.4106e-01, -3.5242e-02,  4.9280e-02, -5.2363e-02,\n",
      "          9.1988e-02,  2.1115e-01,  2.0002e-01,  1.6272e-01,  9.1625e-02,\n",
      "          3.1383e-02,  4.6095e-02, -5.7395e-03,  1.3356e-03, -2.0979e-02,\n",
      "         -6.3265e-03,  1.0557e-02,  2.1936e-02,  1.4461e-02,  6.4191e-02,\n",
      "          9.4519e-02,  6.7036e-02,  1.2278e-01,  1.0997e-01,  1.1183e-01,\n",
      "          1.2986e-01,  2.1177e-01,  1.1551e-01,  2.5075e-01,  1.9432e-01,\n",
      "          1.1389e-01,  1.2179e-01,  1.2343e-01,  1.6421e-01,  2.1207e-01,\n",
      "          1.6832e-01,  2.7935e-01,  3.0499e-01,  2.8583e-01,  2.9104e-01,\n",
      "          3.8417e-02, -1.0493e-01, -2.6406e-02, -5.5351e-04, -9.5744e-03,\n",
      "          1.6762e-02, -9.3853e-02,  6.0120e-02,  4.4883e-02, -3.8232e-02,\n",
      "          8.0821e-02,  5.1597e-02,  3.8764e-02,  3.1925e-02,  1.1520e-02,\n",
      "          6.2301e-03, -2.5681e-02, -2.1731e-02,  6.9665e-03,  7.4653e-02,\n",
      "          1.6810e-02,  1.0785e-01,  9.3203e-02,  1.7015e-01,  1.2845e-01,\n",
      "          1.3289e-01,  2.0868e-01,  2.0668e-01,  1.8325e-01,  2.8148e-02,\n",
      "          4.7976e-02, -2.8584e-02,  1.7864e-02,  2.5192e-02, -8.8602e-02,\n",
      "         -1.8849e-01,  1.1253e-04, -2.3671e-02, -8.7540e-03, -2.5475e-02,\n",
      "         -3.3663e-02, -8.6971e-02, -4.8006e-02, -9.3768e-02, -1.0818e-01,\n",
      "         -8.0791e-02, -5.9709e-02, -7.2854e-02, -4.2907e-02,  3.1579e-02,\n",
      "          1.0864e-02,  3.6178e-02,  1.0386e-01,  7.2957e-02,  1.5063e-01,\n",
      "          1.0049e-01,  3.0446e-02, -3.8992e-02, -9.2353e-02,  1.1153e-01,\n",
      "         -1.9090e-02, -1.7836e-02, -6.8819e-02, -1.4107e-01, -8.7037e-02,\n",
      "         -1.0904e-01, -1.3332e-01, -7.7457e-02, -1.3039e-02, -8.7927e-02,\n",
      "         -3.0244e-02, -1.3401e-01, -6.6719e-02, -1.0317e-01, -8.2223e-02,\n",
      "         -1.0925e-01, -8.6592e-02, -7.8142e-02, -1.0054e-02,  3.2955e-02,\n",
      "          8.9837e-02,  1.5009e-02,  4.4652e-02,  5.9862e-02, -1.9619e-02,\n",
      "         -1.8306e-01, -8.8082e-02,  9.4188e-02,  2.3143e-02, -2.0090e-03,\n",
      "          2.5236e-02, -1.6064e-01, -1.5250e-01, -8.3678e-03, -5.3055e-02,\n",
      "         -2.5834e-02, -1.9948e-02, -6.4109e-02, -8.1864e-02, -5.6840e-02,\n",
      "         -5.2106e-02, -1.0681e-01, -1.1059e-01, -7.9736e-02, -1.4784e-01,\n",
      "         -5.6613e-02, -8.0088e-02, -6.0368e-02, -3.5037e-02, -3.6848e-02,\n",
      "         -4.3500e-02, -5.0437e-02, -1.2511e-01, -1.3752e-01, -1.1474e-01,\n",
      "         -4.1870e-02,  1.2778e-02, -1.3567e-03,  2.0024e-02, -1.9642e-01,\n",
      "         -1.7998e-01, -3.5848e-02, -9.1051e-02,  1.5320e-02, -5.4311e-02,\n",
      "         -4.1941e-02, -5.2212e-02, -2.9644e-02, -5.2452e-02, -1.1791e-01,\n",
      "         -5.0531e-02, -7.3851e-02, -1.3901e-01, -7.6652e-02, -1.7429e-01,\n",
      "         -2.2099e-01, -2.5087e-01, -1.8072e-01, -1.6181e-01, -2.4462e-01,\n",
      "         -1.1418e-01, -1.8651e-01, -2.9790e-02, -8.4313e-02,  8.1225e-03,\n",
      "         -2.0899e-02, -2.3601e-02, -1.3786e-01, -2.0516e-01, -1.5177e-01,\n",
      "         -1.2051e-01, -3.6949e-02, -3.5805e-02,  8.8123e-03, -6.1720e-02,\n",
      "         -6.9530e-02, -7.2376e-02, -8.0996e-02, -1.0172e-01, -1.2294e-01,\n",
      "         -1.4956e-01, -1.5564e-01, -2.8969e-01, -3.6324e-01, -3.5678e-01,\n",
      "         -2.8776e-01, -3.3543e-01, -5.2513e-01, -4.2379e-01, -2.7336e-01,\n",
      "         -1.3107e-01, -1.3997e-01,  3.9298e-04,  3.0962e-02, -1.0851e-01,\n",
      "         -1.1426e-01, -2.2384e-01, -1.7266e-01, -7.0792e-02, -7.6277e-03,\n",
      "         -1.2194e-03, -4.5122e-02, -5.8146e-02, -6.1255e-02, -3.6733e-02,\n",
      "         -8.1232e-02, -8.2141e-02, -1.2112e-01, -1.5518e-01, -2.5272e-01,\n",
      "         -3.0727e-01, -2.0227e-01, -1.7472e-01, -1.3907e-01, -1.7188e-01,\n",
      "         -1.5668e-01, -3.6219e-01, -3.5728e-01, -3.2796e-01, -1.2561e-01,\n",
      "         -6.3905e-03, -1.9892e-02, -1.2781e-01, -1.5278e-01, -3.2325e-01,\n",
      "         -8.5202e-02, -1.7836e-02,  9.1693e-03,  2.1140e-03, -4.8720e-02,\n",
      "         -4.0494e-02, -5.2322e-02, -6.9750e-02, -1.6396e-02, -1.5338e-02,\n",
      "         -1.7147e-01, -1.6606e-01, -1.6280e-01, -9.3856e-02, -9.7804e-02,\n",
      "         -2.4812e-02, -5.6719e-02, -4.9555e-02, -1.8558e-02, -1.5648e-01,\n",
      "         -2.9092e-01, -3.0151e-01, -1.5812e-01, -1.2042e-03,  1.6840e-02,\n",
      "          1.8507e-02, -1.9712e-01, -2.1944e-01, -3.6598e-02,  2.9376e-02,\n",
      "          1.7239e-02, -3.3273e-03,  2.9137e-02,  7.0245e-03, -2.8985e-02,\n",
      "          2.3553e-02,  1.0781e-01, -1.5488e-02, -1.4679e-01, -5.4315e-02,\n",
      "         -9.3611e-02, -7.1194e-02, -1.8568e-02, -1.9693e-02,  6.5482e-02,\n",
      "          7.4795e-02,  1.1646e-01,  3.2595e-02, -1.7413e-01, -4.0290e-01,\n",
      "         -1.8838e-02, -3.1229e-02, -3.2198e-02, -5.0531e-02, -2.3300e-01,\n",
      "         -3.0215e-01, -6.6476e-02,  9.9718e-02,  7.2661e-02,  4.2386e-02,\n",
      "          3.2237e-02,  1.8399e-02,  9.2557e-02,  7.9998e-02,  4.9080e-02,\n",
      "         -6.9957e-02, -6.9725e-02,  1.9941e-03,  1.2408e-02, -6.4808e-03,\n",
      "         -2.1473e-02, -4.9399e-02,  9.8510e-02,  1.0718e-01,  1.6375e-01,\n",
      "          1.6876e-01, -8.7388e-02, -3.8451e-01, -1.1864e-01,  8.3276e-03,\n",
      "         -6.2138e-02, -7.1904e-02, -2.3598e-01, -3.0036e-01,  1.5949e-02,\n",
      "          1.2102e-01,  4.5776e-02,  4.7871e-02,  4.5267e-02,  9.1154e-02,\n",
      "          8.4270e-02,  7.7122e-02,  9.5043e-03, -4.2172e-02,  8.2666e-02,\n",
      "          2.0444e-02, -1.2014e-02, -2.5547e-02,  1.1604e-02, -4.2102e-03,\n",
      "          6.8595e-02,  6.8241e-02,  1.9906e-01,  1.2068e-01, -1.2165e-01,\n",
      "         -4.5981e-01, -1.3674e-01,  2.0999e-02, -2.7046e-02, -1.5172e-02,\n",
      "         -8.7989e-02, -3.7155e-01, -5.8935e-02,  8.3477e-02,  7.8680e-02,\n",
      "          7.7855e-02,  1.2495e-01,  9.0876e-02,  1.1100e-01,  6.6330e-02,\n",
      "         -3.0679e-02, -2.1738e-02,  4.9839e-02,  2.6078e-02,  1.1816e-02,\n",
      "         -5.9750e-02, -2.1861e-02,  5.0303e-02,  4.9828e-02,  8.9100e-02,\n",
      "          1.0046e-01,  1.1414e-02, -2.2620e-02, -3.3758e-01, -8.6613e-02,\n",
      "         -4.3160e-03, -2.2604e-02,  1.0743e-02, -1.6596e-01, -3.0498e-01,\n",
      "         -9.3475e-02,  6.5943e-02,  8.2002e-02,  9.8860e-04,  1.0553e-01,\n",
      "          5.9079e-02,  1.1840e-01,  3.7025e-02, -4.6253e-02,  8.0026e-02,\n",
      "          6.9693e-02,  7.2273e-04,  1.9300e-02, -4.0258e-02, -6.9272e-03,\n",
      "          8.3633e-02,  6.4086e-02, -2.6390e-03,  1.5648e-02,  8.0718e-03,\n",
      "         -4.7141e-02, -3.6747e-01, -1.0941e-01,  2.7627e-02,  1.8325e-02,\n",
      "         -1.8839e-02, -2.0586e-01, -1.9998e-01, -1.0536e-01,  4.7515e-02,\n",
      "          2.9866e-02,  1.5798e-02,  6.9944e-02,  6.4110e-02,  1.8788e-01,\n",
      "          3.3749e-04, -3.8500e-02,  7.8658e-02,  1.5568e-02, -3.5426e-02,\n",
      "         -4.1659e-02, -3.1537e-02,  6.4284e-03,  1.7448e-02,  5.6981e-03,\n",
      "          1.6504e-02, -1.0432e-02, -2.9757e-02, -1.2635e-01, -4.0513e-01,\n",
      "         -9.8617e-02,  1.1941e-02,  2.0174e-02, -6.4189e-02, -8.9797e-02,\n",
      "         -1.9139e-01, -1.2130e-01,  1.6041e-02,  5.4360e-02,  5.8693e-02,\n",
      "          6.5869e-02,  1.4628e-01,  1.6922e-01,  2.9201e-02,  1.4412e-02,\n",
      "          6.4790e-02, -3.2891e-02, -5.8774e-02, -3.7558e-02,  6.7831e-02,\n",
      "          3.3129e-02,  5.1195e-02, -1.2979e-02, -5.5993e-03,  6.8547e-03,\n",
      "         -8.6380e-02, -1.0940e-01, -4.1860e-01, -1.5793e-01,  5.2677e-03,\n",
      "         -3.4298e-02, -1.5460e-02, -1.4215e-01, -1.6994e-01, -1.4995e-01,\n",
      "         -6.2399e-02,  3.2406e-02,  3.7992e-02,  5.9104e-02,  1.0921e-01,\n",
      "          1.7690e-01,  1.7309e-01,  1.0089e-01,  3.7850e-02,  1.6302e-02,\n",
      "          8.6406e-02,  7.0717e-02,  8.3628e-02, -6.3676e-03, -1.9355e-02,\n",
      "          1.0109e-02,  4.0733e-03,  6.7206e-02, -1.4672e-01, -8.5668e-02,\n",
      "         -9.9873e-02, -1.3252e-01,  3.4698e-02, -1.0358e-02, -3.5224e-02,\n",
      "         -2.1151e-01, -8.8741e-02, -1.0322e-01, -1.3685e-01, -1.4561e-02,\n",
      "          7.4697e-03,  7.0591e-02,  1.3274e-01,  1.7744e-01,  1.8655e-01,\n",
      "          6.0083e-02,  6.0906e-02,  9.6571e-02,  6.2869e-02,  3.9051e-02,\n",
      "          2.6310e-02,  8.7576e-03, -5.2429e-03, -1.3471e-02, -3.0228e-02,\n",
      "         -2.2584e-02, -1.2774e-01, -1.9816e-01, -2.3206e-01, -1.1856e-01,\n",
      "          1.7085e-02,  2.4689e-02, -3.4664e-02, -1.5285e-01, -6.9242e-02,\n",
      "         -9.9334e-02, -1.3644e-01, -8.6856e-02, -7.0050e-02,  5.0244e-02,\n",
      "          1.0876e-01,  1.1422e-01,  1.0121e-01,  1.1560e-01,  1.3031e-01,\n",
      "          1.1461e-01,  1.0004e-01,  3.2442e-02,  6.2108e-02,  3.1009e-02,\n",
      "         -7.5415e-03, -5.4519e-02, -1.3610e-01, -5.4878e-02, -1.2440e-01,\n",
      "         -9.1197e-02, -1.1391e-02,  1.8744e-02,  5.0740e-03,  3.3233e-02,\n",
      "          1.7958e-02, -7.5012e-02, -1.8008e-01, -1.8935e-01, -1.5250e-01,\n",
      "         -4.0299e-02,  2.4346e-03, -2.2311e-02,  4.5031e-02,  1.3843e-01,\n",
      "          1.9011e-01,  2.1436e-01,  1.8863e-01,  1.4627e-01,  8.7661e-02,\n",
      "          1.0490e-01,  6.8748e-02,  3.5428e-02, -4.4999e-03, -1.2746e-01,\n",
      "         -1.3565e-01, -9.8283e-02, -9.3309e-02, -3.7897e-02,  2.4967e-02,\n",
      "          2.4842e-02,  2.3866e-02, -3.0635e-02, -1.6025e-03, -2.6390e-02,\n",
      "         -1.4653e-01, -1.8339e-01, -2.5383e-01, -1.1545e-01, -1.2560e-02,\n",
      "          2.0034e-02,  3.3348e-02, -2.5323e-03, -9.0470e-03, -1.2798e-02,\n",
      "          4.0326e-02,  8.9568e-02,  6.2858e-02,  4.8585e-02, -3.9778e-03,\n",
      "          1.1576e-02,  6.6959e-03, -9.5563e-02, -9.6393e-02, -1.9721e-01,\n",
      "         -2.1908e-01, -8.7445e-02,  3.8686e-02, -1.3832e-02, -9.2337e-03,\n",
      "         -2.7257e-02, -2.8162e-02, -1.0492e-02, -5.0474e-02, -8.6047e-02,\n",
      "         -1.0300e-01, -1.8679e-01, -1.4563e-01, -1.0985e-01, -1.7270e-01,\n",
      "         -1.2312e-01, -1.4281e-01, -4.1760e-02, -1.0294e-01, -4.0467e-02,\n",
      "         -1.1761e-01, -1.4468e-01, -4.0923e-02, -9.2421e-03, -1.8750e-02,\n",
      "         -8.6843e-02, -9.0870e-02, -1.9097e-01, -1.8985e-01, -1.9505e-01,\n",
      "         -8.5350e-03, -1.5561e-02,  2.3055e-02, -2.4668e-02, -2.6692e-02,\n",
      "         -3.3594e-02,  7.7240e-03, -2.7320e-02,  4.7787e-04, -1.1233e-01,\n",
      "         -1.2279e-01, -1.3034e-01, -1.7593e-01, -2.2742e-01, -1.7955e-01,\n",
      "         -1.9962e-01, -2.9217e-01, -2.6630e-01, -1.7750e-01, -2.2615e-01,\n",
      "         -2.3333e-01, -2.0666e-01, -1.9756e-01, -8.9328e-02, -5.5745e-02,\n",
      "         -2.3365e-02,  1.3691e-02,  2.5450e-02,  3.3391e-03,  4.1523e-03,\n",
      "         -3.0805e-02,  2.2535e-02,  3.1740e-02,  1.5109e-02,  3.5163e-02,\n",
      "          8.2300e-03, -4.3603e-02, -5.3431e-02, -2.9331e-02, -6.4256e-02,\n",
      "         -1.3292e-01, -9.1929e-02, -4.8363e-02, -2.0078e-01, -1.9868e-01,\n",
      "         -2.2731e-01, -1.5334e-01, -8.2945e-02, -5.2749e-02, -3.1891e-02,\n",
      "         -5.1613e-02,  1.5342e-02,  3.7044e-03,  1.3301e-02, -2.7705e-02,\n",
      "         -2.0211e-02,  9.1721e-03,  3.2870e-02,  2.3281e-02,  3.3640e-02,\n",
      "         -9.3614e-03,  2.5652e-03, -8.0905e-03,  3.0570e-03,  1.9485e-02,\n",
      "         -8.8052e-03,  6.7406e-03, -4.5260e-02, -4.4282e-02, -3.0200e-02,\n",
      "          1.5569e-02, -2.6173e-02, -1.7120e-02, -6.4720e-02, -2.5327e-02,\n",
      "          1.6885e-02,  9.6624e-03, -2.0479e-03, -5.0781e-02, -4.1249e-02,\n",
      "         -3.1286e-02, -2.3233e-02,  1.9001e-02, -2.6903e-03,  2.6176e-02,\n",
      "          3.4666e-02,  3.4820e-02, -2.1032e-02,  1.6555e-03, -3.2250e-02,\n",
      "          1.2298e-03,  2.8211e-02,  2.9532e-02, -1.3161e-02,  1.7374e-02,\n",
      "          4.5981e-03, -2.4938e-02, -2.0865e-02, -3.3413e-02,  3.5656e-03,\n",
      "         -5.9217e-03, -4.2776e-03, -3.1809e-02, -3.4740e-02, -3.0098e-02,\n",
      "         -1.0875e-02, -2.5463e-02,  3.4409e-02,  2.6032e-02,  2.3524e-02,\n",
      "          3.2760e-02,  2.6210e-02, -1.8386e-02, -2.6821e-02],\n",
      "        [-1.2341e-02, -1.4089e-02, -6.1041e-03, -7.8240e-03, -2.7911e-02,\n",
      "          1.0725e-02,  2.5802e-02, -2.3471e-02,  3.5397e-02, -1.2829e-02,\n",
      "          1.9239e-02, -5.9528e-03,  2.3501e-02, -1.6414e-03, -3.2549e-02,\n",
      "          1.9643e-02, -1.5239e-02, -1.0015e-02, -1.5528e-03,  2.0688e-02,\n",
      "         -2.9933e-02, -1.2767e-02,  2.6098e-02,  1.9344e-02, -3.1517e-02,\n",
      "         -3.5186e-02,  8.9828e-03, -2.2891e-02,  5.3572e-03, -2.9791e-03,\n",
      "         -3.2350e-02, -1.1008e-02, -1.7425e-02, -1.0778e-02,  3.4934e-02,\n",
      "         -2.2428e-02,  1.7205e-02, -7.6521e-05,  2.3141e-02, -9.5004e-02,\n",
      "         -1.1127e-01, -5.4944e-02,  2.3109e-02, -1.7498e-02,  1.8936e-03,\n",
      "         -2.0072e-02,  3.5011e-02,  6.1269e-03, -2.3571e-02, -1.5894e-02,\n",
      "         -3.1925e-02,  2.0601e-02,  2.5407e-02,  2.6690e-02,  2.5364e-03,\n",
      "          3.3338e-02,  1.2653e-02,  1.0268e-02, -3.0813e-02, -1.6810e-02,\n",
      "         -1.1666e-02, -2.4045e-03, -1.5341e-02,  2.8012e-03, -4.4238e-02,\n",
      "         -3.3465e-02, -3.2322e-02, -9.0738e-02, -1.1814e-01, -9.0587e-02,\n",
      "         -3.8694e-02, -5.2308e-02, -5.1539e-02, -4.3748e-02,  1.7743e-03,\n",
      "         -1.2662e-02,  1.3147e-02, -2.8000e-02,  3.1311e-02, -1.6671e-02,\n",
      "         -3.0414e-02, -1.1181e-04, -2.5584e-02,  9.1789e-03, -5.0988e-03,\n",
      "          2.9159e-03, -4.7532e-03, -1.3213e-02, -7.5282e-03, -2.8536e-02,\n",
      "          2.4119e-02, -6.9259e-02, -8.6879e-02, -2.1128e-02, -7.9742e-02,\n",
      "         -1.3753e-01, -1.0959e-01, -1.8122e-01, -1.4014e-01, -1.1180e-01,\n",
      "         -7.4716e-02, -1.0242e-01, -8.4547e-02, -3.5361e-02, -4.5822e-02,\n",
      "         -4.3890e-02, -5.2570e-03,  2.2425e-02, -1.0968e-02,  1.0481e-02,\n",
      "         -3.1125e-02, -1.5749e-02, -9.0642e-03,  2.7912e-02,  6.8132e-03,\n",
      "          8.2316e-02, -1.5535e-02, -8.9337e-02, -9.3390e-02, -5.0172e-02,\n",
      "         -1.7630e-01, -1.6207e-01, -2.2231e-01, -2.4361e-01, -1.8821e-01,\n",
      "         -1.6736e-01, -2.9313e-01, -1.9929e-01, -1.7064e-01, -2.0880e-01,\n",
      "         -1.7131e-01, -1.3379e-01, -6.4878e-02,  2.7936e-02, -6.8331e-02,\n",
      "         -3.5882e-02,  2.3767e-02,  2.1804e-02, -7.7787e-04, -3.0429e-02,\n",
      "         -2.3606e-02, -2.3247e-02,  6.9294e-05,  4.2879e-02, -7.8058e-02,\n",
      "          2.5385e-02, -7.7711e-03, -4.5327e-02, -6.7620e-02, -8.8141e-02,\n",
      "         -1.5631e-01, -2.1422e-01, -2.0421e-01, -1.3458e-01, -1.4046e-01,\n",
      "         -1.3816e-01, -1.7849e-01, -3.1096e-01, -2.5690e-01, -3.1092e-01,\n",
      "         -3.3683e-01, -2.4877e-01, -1.7760e-01, -2.3688e-01, -8.9648e-02,\n",
      "         -6.6749e-03, -3.3161e-02, -1.3669e-02, -1.4778e-02,  2.6470e-02,\n",
      "          1.2012e-01,  1.1984e-01, -5.2698e-02,  1.5217e-02,  4.6408e-02,\n",
      "          3.3001e-02,  6.3520e-02,  8.2013e-02,  8.0050e-02,  1.1179e-01,\n",
      "          2.1136e-02, -2.1987e-02, -9.2279e-02, -1.1227e-02,  3.0962e-02,\n",
      "          2.6296e-02, -2.1880e-02, -3.4290e-02, -2.7915e-02,  5.4111e-02,\n",
      "         -1.4206e-02, -1.4578e-01, -7.3031e-02, -1.5239e-01, -7.6876e-02,\n",
      "         -2.4421e-02, -1.1700e-02,  8.2848e-02,  1.8442e-01,  1.6998e-01,\n",
      "         -1.2064e-02,  8.3090e-02,  1.2491e-01,  7.8513e-02,  3.2483e-02,\n",
      "          9.3549e-02,  1.4234e-01,  1.0702e-01,  1.2755e-01,  2.4004e-02,\n",
      "         -3.3112e-02, -6.1041e-02,  4.9912e-02,  6.7524e-02,  6.0753e-02,\n",
      "          5.2171e-02,  9.1672e-02,  5.0763e-02,  5.5726e-02,  4.4959e-02,\n",
      "         -1.9342e-02, -2.5551e-01, -2.3520e-01,  6.5672e-03, -1.0892e-01,\n",
      "          1.3972e-01,  2.1805e-01,  1.9499e-01,  5.3628e-02,  7.2113e-02,\n",
      "          9.0058e-02,  1.7918e-01,  1.5375e-01,  1.2493e-01,  1.1277e-01,\n",
      "          1.3572e-01,  1.1385e-01,  7.3906e-02,  7.1566e-02,  6.1196e-02,\n",
      "          9.1007e-02,  9.8995e-02,  1.1435e-01,  5.6498e-02,  4.0118e-02,\n",
      "          1.2379e-03,  1.2734e-01,  9.4005e-02, -9.2555e-02, -2.4684e-01,\n",
      "         -2.3382e-01, -2.3061e-02,  7.9865e-02,  1.6662e-01,  1.9525e-01,\n",
      "          1.5578e-01,  1.4141e-01,  1.2842e-01,  4.6628e-02,  6.8273e-02,\n",
      "          5.8031e-02,  3.5392e-02,  5.6042e-02,  8.7793e-02,  1.2351e-01,\n",
      "          6.7748e-02,  1.2650e-01,  1.1792e-01,  1.6580e-01,  1.2605e-01,\n",
      "          6.7840e-02,  9.5956e-02,  3.1972e-02,  3.7113e-02,  6.4543e-02,\n",
      "         -1.3983e-02, -5.8049e-02, -2.1698e-02, -1.2512e-01,  7.8608e-02,\n",
      "          7.7141e-02,  1.5287e-01,  2.5570e-01,  1.4241e-01,  8.3782e-02,\n",
      "          1.4101e-01,  4.0037e-02,  1.1396e-02,  1.4433e-02,  6.6047e-03,\n",
      "          1.8329e-02,  4.7245e-02,  3.6877e-02,  1.6836e-01,  1.7727e-01,\n",
      "          2.1919e-01,  2.2499e-01,  1.7754e-01,  1.4072e-01,  1.1287e-01,\n",
      "          1.1570e-01,  6.4033e-02,  1.4314e-01, -1.6388e-02, -9.5166e-02,\n",
      "          1.2240e-03, -1.4115e-01,  2.9646e-02,  3.7355e-02,  2.0424e-01,\n",
      "          3.2461e-01,  2.6843e-01,  1.9677e-01,  1.1722e-01, -2.5199e-02,\n",
      "         -2.5223e-02, -3.6633e-02, -3.2260e-02,  1.0789e-02,  3.8370e-02,\n",
      "          5.3516e-02,  7.2540e-02,  9.5468e-02,  1.9092e-01,  2.1906e-01,\n",
      "          1.2632e-01,  1.1354e-01,  4.3764e-02,  2.6744e-02,  3.4598e-02,\n",
      "          4.2404e-02, -7.7436e-02, -1.9363e-01, -6.0148e-02, -1.1256e-01,\n",
      "          8.8563e-02,  3.5560e-02,  1.8603e-01,  3.8847e-01,  2.5476e-01,\n",
      "          2.2077e-01,  5.3858e-02, -5.0796e-02,  4.4264e-02,  1.4732e-02,\n",
      "         -4.6811e-02, -3.1496e-02, -5.1469e-02, -1.9170e-01, -3.7191e-01,\n",
      "         -2.1558e-01,  3.9557e-02,  1.1692e-01,  1.0854e-01,  2.4692e-02,\n",
      "         -6.1617e-02, -3.4152e-02, -5.8484e-02, -2.9190e-02, -1.0339e-01,\n",
      "         -3.1813e-01, -2.0003e-02, -5.4156e-02,  1.2264e-01,  1.1026e-04,\n",
      "          1.5232e-01,  2.9758e-01,  1.9877e-01,  1.1775e-01,  3.6862e-02,\n",
      "         -9.1387e-02, -5.9919e-02, -5.1411e-02, -1.0782e-01, -5.0308e-02,\n",
      "         -1.4492e-01, -3.6281e-01, -5.4334e-01, -2.4128e-01, -6.2855e-02,\n",
      "          1.8031e-02, -2.2513e-02, -2.5091e-02, -7.7681e-02, -1.6699e-02,\n",
      "         -6.6260e-03,  3.1481e-02,  8.4117e-03, -1.3013e-01, -1.1908e-01,\n",
      "         -9.5759e-02,  3.5714e-02, -1.6507e-02,  9.9611e-02,  2.2459e-01,\n",
      "          1.2455e-01, -6.1721e-02,  1.0078e-02, -1.1859e-01, -9.5649e-02,\n",
      "         -9.5695e-02, -8.1371e-02, -6.8117e-02, -2.2825e-01, -3.4960e-01,\n",
      "         -2.9259e-01, -3.7207e-02, -5.8811e-02,  1.0573e-03,  6.8777e-02,\n",
      "          9.5598e-02,  1.4103e-01,  7.9700e-02,  8.6354e-02,  2.0130e-01,\n",
      "          1.0919e-01, -6.3185e-02,  3.5186e-02, -1.3358e-01,  4.1323e-03,\n",
      "          3.2460e-03, -1.9534e-02,  1.3196e-01,  1.0207e-01, -1.1142e-01,\n",
      "         -1.1764e-01, -8.0528e-02, -1.0730e-01, -1.6400e-01, -1.1090e-01,\n",
      "         -1.1014e-01, -1.8856e-01, -2.6393e-01, -1.9721e-01, -6.1959e-02,\n",
      "          2.2183e-02,  1.0992e-02,  1.3526e-01,  1.5445e-01,  1.5052e-01,\n",
      "          1.5606e-01,  1.8683e-01,  1.9654e-01,  6.9672e-02, -8.5541e-02,\n",
      "         -2.1065e-01, -1.3273e-01,  3.1965e-02,  7.7474e-02, -4.1055e-02,\n",
      "         -3.0748e-02,  5.3274e-02, -3.3965e-02, -7.2512e-02, -3.0746e-02,\n",
      "          3.0417e-02, -7.8771e-02, -8.8282e-02, -1.6475e-01, -2.4803e-01,\n",
      "         -1.0502e-01, -6.2173e-02, -6.1285e-02,  4.0429e-02,  4.2911e-02,\n",
      "          1.1691e-01,  1.0500e-01,  1.3448e-01,  1.1075e-01,  1.0092e-01,\n",
      "         -4.4812e-02, -7.1930e-02, -1.0914e-01, -3.1944e-01, -1.3846e-01,\n",
      "          1.0792e-02,  1.0751e-02, -1.0629e-02,  6.5894e-02,  4.8541e-02,\n",
      "         -2.7359e-02,  2.1726e-02, -6.2980e-02, -5.1006e-02, -1.0214e-01,\n",
      "         -9.9347e-02, -2.8804e-01, -1.5793e-01, -3.8266e-02,  2.3428e-02,\n",
      "          3.2403e-02,  1.7755e-02, -3.8101e-02,  2.8339e-02,  6.5282e-02,\n",
      "          1.0213e-01,  5.9171e-02,  3.4823e-03, -6.3112e-02, -3.2658e-02,\n",
      "          2.5093e-02, -6.5627e-02, -8.9575e-02,  5.7071e-03,  4.9944e-02,\n",
      "         -3.3942e-02,  1.4457e-01,  7.7700e-02, -7.4141e-02, -1.2443e-02,\n",
      "         -1.8466e-01, -1.2106e-01, -1.4682e-01, -1.2522e-01, -2.0154e-01,\n",
      "         -7.9419e-02,  1.2441e-02,  1.1124e-01,  6.9124e-02, -1.8733e-02,\n",
      "         -4.6327e-02,  1.8458e-02, -7.8452e-03, -6.0307e-02, -4.7966e-02,\n",
      "         -1.5190e-01, -1.7279e-01, -2.2774e-01, -2.2877e-01, -6.1215e-02,\n",
      "         -3.9311e-02,  3.0026e-02, -2.9004e-03,  1.4172e-01,  1.5381e-01,\n",
      "          2.4549e-02, -1.8283e-02, -1.4906e-01, -2.9336e-01, -2.9413e-01,\n",
      "         -1.8801e-01, -1.5819e-01, -1.1995e-01, -3.6518e-02,  8.3975e-02,\n",
      "          8.7389e-02,  3.7346e-02, -1.9893e-02, -1.1986e-01, -1.0158e-01,\n",
      "         -1.0351e-01, -1.3954e-01, -1.8546e-01, -2.5342e-01, -2.8712e-01,\n",
      "         -3.0145e-01, -1.5413e-01, -1.8862e-02, -2.2080e-02, -1.6103e-02,\n",
      "          7.1064e-03,  3.5680e-02, -2.5889e-02, -1.7811e-01, -2.0043e-01,\n",
      "         -2.8766e-01, -4.2028e-01, -3.6709e-01, -1.9173e-01, -9.1341e-02,\n",
      "         -1.8249e-02, -2.8410e-02,  2.3276e-02,  2.9470e-03, -5.0744e-03,\n",
      "         -6.9452e-02, -1.2299e-01, -1.4220e-01, -1.7355e-01, -2.1361e-01,\n",
      "         -2.4460e-01, -3.0184e-01, -3.4284e-01, -3.5310e-01, -1.0916e-01,\n",
      "          5.5017e-02, -3.2549e-02, -5.1926e-03, -6.0003e-03, -7.3157e-05,\n",
      "         -1.6894e-01, -3.2573e-01, -3.2529e-01, -2.5558e-01, -2.1512e-01,\n",
      "         -1.7611e-01, -6.6679e-02, -4.6805e-02, -4.0718e-02, -5.1436e-02,\n",
      "         -2.1086e-02, -1.4230e-02, -5.3275e-02, -6.5482e-02, -9.4866e-02,\n",
      "         -1.1775e-01, -1.6997e-01, -2.1867e-01, -2.0056e-01, -2.2996e-01,\n",
      "         -3.4609e-01, -3.0579e-01, -8.4374e-03,  6.7255e-02, -1.4772e-01,\n",
      "          1.5229e-02, -5.5139e-03,  2.9074e-02, -2.2603e-01, -3.8410e-01,\n",
      "         -3.1543e-01, -5.0009e-02, -1.0047e-01,  5.2345e-02,  5.7468e-03,\n",
      "         -6.7854e-02, -7.0078e-02, -8.7732e-02, -5.8324e-02, -6.8382e-02,\n",
      "         -5.9135e-02, -3.1409e-02, -5.5836e-02, -1.3208e-01, -1.0279e-01,\n",
      "         -1.2790e-01, -1.0319e-01, -2.0867e-01, -3.2190e-01, -2.2527e-01,\n",
      "         -7.4752e-02,  6.7032e-02, -3.5134e-02, -8.2431e-03, -5.2333e-03,\n",
      "         -3.2246e-02, -2.0420e-01, -3.6136e-01, -3.8902e-02,  5.5362e-02,\n",
      "          1.3084e-01,  7.2597e-02, -1.0556e-01, -1.0414e-01, -5.3740e-02,\n",
      "         -9.1551e-02, -1.1796e-01, -8.0889e-02, -1.6966e-02, -2.6794e-02,\n",
      "         -5.5148e-02, -7.1605e-02, -1.0619e-01, -1.5027e-01, -1.2869e-01,\n",
      "         -2.5097e-01, -2.9488e-01, -2.6828e-01, -9.9737e-02,  1.2026e-02,\n",
      "          6.8818e-03, -2.5102e-02,  2.6163e-02, -1.1690e-03, -1.3642e-01,\n",
      "         -7.3433e-02,  1.7691e-01,  2.8557e-01,  1.8438e-01,  6.9460e-02,\n",
      "          7.4975e-03,  1.7221e-03, -2.6977e-03, -1.7438e-04, -8.9034e-03,\n",
      "          4.9982e-04,  1.2867e-02,  6.4943e-02, -1.7271e-03, -3.4060e-03,\n",
      "         -1.0089e-01, -6.0446e-02, -7.2101e-02, -6.2079e-02, -2.2473e-01,\n",
      "         -2.9611e-01, -1.5089e-01, -7.2582e-02,  1.5029e-02,  4.0257e-03,\n",
      "         -2.9517e-03, -1.2780e-02, -1.5846e-01,  8.9960e-02,  2.4773e-01,\n",
      "          1.6087e-01,  4.3773e-02,  8.3942e-02,  8.2368e-02,  1.0143e-01,\n",
      "          4.6605e-02,  8.3566e-02,  8.1343e-02,  5.1584e-02,  6.1058e-02,\n",
      "          8.7173e-02,  7.2893e-02,  1.1367e-01,  7.7265e-02, -2.2052e-02,\n",
      "         -9.5766e-02,  1.9354e-02,  3.7895e-02, -3.3384e-02,  1.9165e-02,\n",
      "          4.0212e-03,  2.1806e-02, -1.3196e-02,  5.3140e-03,  2.5066e-02,\n",
      "         -2.8169e-02, -1.7193e-02, -4.3012e-02,  2.7455e-02, -4.7728e-03,\n",
      "         -1.0517e-03,  9.2531e-02,  7.2639e-02,  7.4946e-02,  4.7534e-02,\n",
      "          2.3850e-02,  7.8753e-02,  6.2153e-02,  1.8092e-01,  1.2131e-01,\n",
      "          1.5156e-01,  1.8243e-01,  8.5074e-02, -5.0697e-02,  2.1626e-02,\n",
      "          1.2927e-01,  1.8450e-02, -3.1641e-02, -2.7068e-02, -1.4328e-02,\n",
      "          2.3351e-02,  2.3166e-03,  4.6928e-03, -1.7133e-02, -1.5620e-02,\n",
      "         -7.5645e-03, -1.3082e-02, -2.5806e-02, -1.3587e-02,  5.1313e-02,\n",
      "         -7.2445e-02,  2.4074e-01,  1.0075e-01,  1.2354e-01,  2.8432e-01,\n",
      "          2.8699e-01,  2.2785e-01,  1.8329e-01,  3.7121e-01,  2.6174e-01,\n",
      "          1.9137e-01,  6.1362e-02,  1.6874e-01,  1.0470e-01,  1.3365e-01,\n",
      "          1.4453e-02, -2.7603e-02,  5.9373e-04,  7.2110e-03],\n",
      "        [-2.5340e-02, -1.4049e-02,  5.5712e-03,  2.5925e-02, -1.1252e-02,\n",
      "          2.4809e-03,  3.2601e-03,  2.4372e-02,  4.5718e-02, -2.2538e-02,\n",
      "          3.3986e-03, -2.7626e-02,  1.6527e-02,  7.5782e-03, -1.0746e-02,\n",
      "         -1.6559e-02, -8.7387e-03, -1.9189e-05, -1.6557e-02,  2.2072e-02,\n",
      "          2.0220e-02,  1.8436e-02,  1.9529e-02, -1.9150e-02, -8.6490e-03,\n",
      "          2.6426e-02, -3.4477e-02,  1.2072e-02, -2.0519e-02, -3.5395e-02,\n",
      "          1.4678e-02, -1.8244e-02,  4.5272e-03,  8.6529e-03,  1.6013e-03,\n",
      "         -1.8925e-02, -3.3186e-02, -8.9603e-03, -2.7525e-02,  1.4818e-02,\n",
      "          9.1906e-03, -1.9918e-02, -1.6260e-02,  1.0960e-02, -7.8818e-02,\n",
      "         -1.2645e-01, -9.2612e-02, -5.2007e-02, -5.8057e-02, -7.7448e-02,\n",
      "         -7.6597e-02, -1.7211e-02, -8.7610e-03, -6.8951e-03,  6.4098e-03,\n",
      "         -8.6174e-03,  2.0315e-02, -1.2941e-02,  5.3667e-03, -1.1899e-02,\n",
      "         -3.7334e-03,  1.3529e-02, -2.9269e-02, -9.4579e-02, -6.9359e-02,\n",
      "         -9.5113e-02, -3.6782e-02, -4.2656e-02, -1.2227e-01, -1.7606e-01,\n",
      "         -1.5089e-01, -1.0532e-01, -1.9047e-01, -1.4600e-01, -2.0653e-02,\n",
      "         -1.7287e-01, -1.2811e-01, -1.3528e-01, -1.1238e-01, -1.4611e-01,\n",
      "         -1.4564e-01, -1.3827e-01,  1.5372e-02,  2.9435e-02, -3.3108e-02,\n",
      "          2.3107e-02,  2.8672e-03, -8.2866e-02, -2.2271e-02,  1.7636e-02,\n",
      "         -5.2534e-02, -8.6223e-02, -1.5260e-01, -1.8035e-01, -1.0428e-01,\n",
      "         -2.0794e-01, -1.7692e-01, -1.8715e-01, -2.2630e-01, -1.7363e-01,\n",
      "         -2.0796e-01, -2.0540e-01, -1.1040e-01, -1.2994e-01, -4.8705e-02,\n",
      "          1.4640e-02,  7.4802e-02,  1.6585e-01,  3.0740e-02, -8.3618e-02,\n",
      "         -5.1876e-02, -5.2900e-03, -6.9350e-03,  1.0124e-02,  1.0173e-01,\n",
      "         -7.7166e-02, -5.3334e-02, -5.7366e-02, -1.9911e-01, -1.5100e-01,\n",
      "         -3.9590e-02,  5.8290e-03,  3.6975e-02, -6.5077e-03,  2.1122e-02,\n",
      "          4.2293e-03,  1.3293e-02,  1.1256e-02, -1.4168e-02, -2.9247e-02,\n",
      "         -3.8064e-02, -6.1895e-02, -2.6747e-02, -5.2702e-02, -3.6087e-02,\n",
      "         -5.6344e-02,  7.3532e-02,  1.0914e-01,  1.0075e-01,  1.5996e-02,\n",
      "          5.5185e-03, -3.3835e-02,  1.4458e-02, -1.9025e-01, -2.1174e-01,\n",
      "         -1.5093e-01, -8.2773e-02, -4.4394e-02, -8.2277e-02, -1.2200e-02,\n",
      "         -7.4086e-02,  6.6717e-03,  3.2604e-02,  2.3537e-02,  5.0191e-02,\n",
      "          1.9872e-02,  6.1222e-02,  6.1240e-02,  2.0201e-02,  2.6078e-02,\n",
      "          6.9101e-03, -5.6846e-02, -5.9416e-02, -1.0635e-01, -9.6756e-02,\n",
      "          6.1615e-02,  7.4985e-02, -2.5740e-02,  3.1440e-02, -6.5123e-03,\n",
      "         -5.7962e-02, -1.8871e-01, -1.6783e-01, -6.2406e-02, -9.5774e-02,\n",
      "         -9.4758e-02, -3.9167e-02, -3.3656e-02, -4.9331e-02, -1.0847e-02,\n",
      "          3.5661e-02,  2.5666e-02, -2.0123e-02,  7.5945e-02,  2.8067e-02,\n",
      "          4.0542e-02, -5.2877e-03,  1.0553e-02,  1.1596e-02,  1.8414e-02,\n",
      "         -2.4934e-02, -1.9490e-02, -7.6647e-02, -2.2090e-02, -5.5862e-02,\n",
      "          6.7517e-03, -2.5387e-02, -1.1258e-01, -1.1373e-01, -1.8686e-01,\n",
      "         -3.1376e-02, -5.1548e-02, -6.2469e-02, -2.3351e-02,  3.2163e-02,\n",
      "         -2.1985e-02,  2.5577e-03, -1.6732e-02, -5.5078e-02, -2.0551e-02,\n",
      "         -4.7585e-02,  1.6289e-02, -1.1355e-03, -3.2136e-02, -5.5102e-03,\n",
      "         -4.1827e-02,  7.3099e-04,  3.0676e-02, -4.9187e-03,  8.4262e-02,\n",
      "          2.9987e-02, -1.2658e-01, -1.0760e-01,  3.6894e-02,  1.9590e-02,\n",
      "         -1.1839e-01, -9.0278e-02, -3.4424e-02,  4.3330e-02,  2.7326e-03,\n",
      "          6.6003e-02,  3.6707e-02, -3.2443e-02,  4.9078e-02,  1.7447e-02,\n",
      "         -1.1326e-02,  3.2475e-02,  2.2472e-02, -3.9540e-02, -2.6008e-02,\n",
      "          1.9637e-02, -6.0363e-02, -7.0416e-03,  3.4915e-02, -6.4976e-03,\n",
      "          5.1042e-02,  5.2135e-02,  5.1724e-02,  3.4160e-02, -1.0828e-01,\n",
      "         -1.2435e-02,  1.3870e-01,  1.3304e-02, -1.3841e-01, -1.2633e-01,\n",
      "         -4.6029e-02, -1.2736e-02, -6.2902e-03,  4.6424e-02,  7.1617e-02,\n",
      "          2.7975e-02,  8.7268e-02,  3.7609e-02,  7.2981e-02,  1.2636e-02,\n",
      "         -4.3420e-03, -9.6320e-02, -1.1708e-01, -3.2975e-02,  2.0924e-02,\n",
      "          3.8020e-03,  6.9350e-03,  2.1977e-02,  3.3794e-02,  9.0874e-02,\n",
      "          6.3130e-02, -3.7049e-02, -1.5677e-01,  7.7096e-02, -1.1155e-01,\n",
      "         -1.9990e-02, -6.4595e-02, -1.3480e-01, -1.8349e-01,  2.3381e-02,\n",
      "         -4.0564e-02,  8.4887e-02,  5.0115e-02,  7.3224e-02,  6.8813e-02,\n",
      "          9.5849e-02,  7.9974e-02,  8.7181e-02,  6.5914e-03, -9.7143e-02,\n",
      "         -1.5527e-01, -1.0909e-01, -1.1457e-03,  1.1175e-02,  5.5188e-02,\n",
      "          4.8694e-02,  8.2811e-02,  7.9973e-02,  1.3772e-01, -3.2588e-02,\n",
      "         -1.5357e-01,  5.1834e-04,  6.5815e-03,  1.6981e-02, -7.9035e-02,\n",
      "         -1.2043e-01, -2.9807e-01, -1.2294e-01,  6.5344e-02,  9.1070e-02,\n",
      "          1.2694e-01,  8.3639e-02,  9.5133e-02,  8.3020e-02,  5.2753e-02,\n",
      "          1.1033e-01,  1.3606e-01,  5.1884e-02, -2.8951e-02, -4.9941e-02,\n",
      "         -1.1609e-02,  2.1683e-02,  3.3803e-03,  3.8965e-03,  2.2074e-02,\n",
      "          1.1560e-01,  1.3484e-01,  2.0798e-01,  1.1021e-02,  5.4857e-02,\n",
      "          2.5605e-02, -3.1724e-02, -8.1044e-03, -1.7882e-01, -6.7519e-02,\n",
      "         -1.1768e-01,  8.8623e-02,  1.4041e-01,  8.9952e-02,  9.6152e-03,\n",
      "          5.9246e-02, -2.9852e-02,  6.9017e-03,  8.9578e-02,  7.8711e-02,\n",
      "          6.1023e-02,  2.8998e-02,  2.5850e-02,  1.7835e-02, -1.5685e-02,\n",
      "          8.2473e-02,  7.6882e-02,  8.3150e-02,  7.8512e-02,  1.8481e-01,\n",
      "          1.9267e-01,  1.2064e-02, -8.2909e-03, -2.0786e-01,  1.7809e-02,\n",
      "          5.0581e-03, -1.0295e-01, -8.0589e-02, -2.0612e-01, -3.2180e-02,\n",
      "         -1.4737e-03, -7.1116e-03,  6.4893e-03, -3.6589e-02, -9.3307e-02,\n",
      "          8.4679e-02,  7.7036e-02,  1.0145e-01,  1.5227e-02,  5.9784e-02,\n",
      "          8.0961e-02, -1.3716e-02, -1.1699e-02, -7.3143e-03,  5.5073e-02,\n",
      "          9.2362e-03,  6.5162e-03, -2.4663e-02, -3.0826e-02, -1.4770e-02,\n",
      "         -1.2790e-02, -7.8539e-02, -1.0408e-02, -4.8688e-03, -4.8496e-02,\n",
      "         -1.6478e-01, -2.1899e-01, -1.4502e-01, -2.1889e-01, -1.9625e-01,\n",
      "         -1.7031e-01, -1.5834e-01, -5.4353e-02,  1.2338e-01,  3.4756e-02,\n",
      "          1.0756e-01,  6.8053e-02,  1.0511e-02,  4.5597e-02,  3.3981e-02,\n",
      "         -8.2215e-02, -6.1510e-02, -6.8776e-02, -7.1564e-02, -1.6367e-01,\n",
      "         -1.8514e-01, -1.7784e-01, -4.4464e-02, -5.4818e-02, -1.0279e-01,\n",
      "         -1.6567e-02, -8.6399e-03, -8.5215e-02,  2.8912e-04, -1.2182e-01,\n",
      "         -1.8886e-01, -2.6207e-01, -2.0890e-01, -1.7384e-01, -6.8601e-02,\n",
      "          6.2163e-03,  6.8341e-02,  2.8876e-02,  1.2780e-01,  9.0230e-02,\n",
      "         -1.7157e-02,  3.1679e-02, -5.5048e-02, -9.8893e-02, -6.4406e-02,\n",
      "         -9.7066e-02, -1.0144e-01, -1.2056e-01, -8.2003e-02, -1.2548e-01,\n",
      "         -8.5926e-02, -1.5233e-01, -2.3885e-02, -1.2919e-02, -5.6438e-02,\n",
      "         -1.3699e-01,  2.8981e-02, -2.1865e-01, -1.3623e-01, -1.0703e-01,\n",
      "         -3.2479e-02, -1.2834e-02,  2.3976e-02,  4.4703e-02,  3.4298e-02,\n",
      "          8.7559e-02,  6.8758e-02, -4.7900e-03,  1.1715e-02,  4.9416e-02,\n",
      "         -7.9013e-02, -1.2495e-01, -1.0321e-01, -1.0677e-01, -1.3026e-01,\n",
      "         -1.0094e-01, -6.3915e-03, -5.6385e-02, -2.3690e-01, -1.6273e-01,\n",
      "         -5.1124e-02, -2.8595e-02, -6.7501e-02, -8.1702e-02,  3.7474e-03,\n",
      "         -1.3023e-01, -1.3626e-01, -7.5591e-02,  5.0390e-02,  8.7310e-02,\n",
      "          7.9618e-02,  2.4389e-02,  9.7695e-02,  1.2720e-01,  6.2943e-02,\n",
      "         -1.2764e-02, -2.1191e-02, -9.8046e-03, -5.5658e-02, -4.4801e-02,\n",
      "         -4.9422e-02,  1.4706e-02, -5.5803e-02, -1.9250e-02,  4.9596e-02,\n",
      "         -2.1407e-02, -4.3580e-01, -1.9328e-01, -4.2421e-03, -2.9415e-02,\n",
      "         -9.9198e-02,  5.7641e-03, -2.0280e-01, -1.8820e-01, -8.2916e-02,\n",
      "         -4.4918e-02,  5.5817e-03,  8.1880e-02,  9.9553e-02,  1.0047e-01,\n",
      "          1.0471e-01,  1.0336e-01, -3.6523e-03, -4.2787e-02, -2.4124e-02,\n",
      "         -2.0543e-02, -3.2295e-02, -1.6945e-02,  1.3030e-02, -2.9669e-02,\n",
      "         -4.3884e-02,  3.5935e-02, -4.3970e-02, -1.8486e-01, -4.1155e-01,\n",
      "         -1.5619e-01, -9.0364e-02, -1.4468e-02, -5.9488e-02, -3.2303e-02,\n",
      "         -1.8351e-01, -1.8611e-01, -5.0952e-02, -7.7457e-02,  1.5173e-02,\n",
      "          2.9261e-02,  6.8549e-02,  8.7644e-02,  1.6083e-02,  3.3654e-02,\n",
      "         -9.4584e-02, -9.4744e-02, -3.9324e-03,  4.4982e-03,  2.1491e-03,\n",
      "         -1.6954e-02, -4.0596e-02, -2.9633e-02, -6.7795e-02,  6.2270e-02,\n",
      "          1.0295e-02, -2.3794e-01, -4.8528e-01, -1.2805e-01, -9.9349e-02,\n",
      "         -1.6166e-02,  2.5272e-02, -1.0997e-01, -1.6123e-01, -1.5819e-01,\n",
      "         -2.1302e-02, -1.7186e-02,  2.1825e-02,  5.3577e-03, -4.6504e-02,\n",
      "          1.2908e-03, -6.8200e-02,  1.8135e-03, -4.4518e-02, -7.5955e-02,\n",
      "         -1.8911e-02, -4.7966e-02, -4.2206e-02,  1.1933e-02, -1.4398e-02,\n",
      "         -1.1836e-02, -6.2331e-03,  3.2756e-02,  1.9630e-02, -2.8452e-01,\n",
      "         -3.3011e-01, -1.4591e-01, -1.1561e-01,  2.8075e-02,  1.6834e-02,\n",
      "         -1.3893e-01, -1.4417e-01, -2.2478e-01, -3.9082e-05,  2.1130e-02,\n",
      "          4.3881e-03, -3.9402e-02, -2.5914e-02, -3.9539e-02, -5.0542e-02,\n",
      "         -2.9516e-02,  1.9359e-02, -2.5736e-02, -5.0184e-02, -4.7663e-02,\n",
      "         -5.6993e-02, -2.2299e-03, -6.0042e-03,  4.4771e-03, -1.3208e-02,\n",
      "          5.5036e-02, -1.1401e-01, -2.7867e-01, -2.5853e-01, -2.0726e-01,\n",
      "         -8.3991e-02, -1.1555e-02, -2.0308e-02, -1.2004e-01, -3.8161e-02,\n",
      "         -2.0471e-01,  2.8034e-03,  2.1472e-02,  2.6779e-03, -5.7791e-02,\n",
      "         -6.0130e-02,  4.3275e-03,  2.9539e-02,  6.1728e-02,  6.0841e-02,\n",
      "          5.9313e-02,  1.8663e-02, -1.0744e-02, -1.9098e-02, -3.2904e-02,\n",
      "         -2.8477e-02, -8.3134e-02,  1.1477e-02, -4.3066e-02, -1.4750e-01,\n",
      "         -2.1639e-01, -1.8955e-01, -6.7918e-02, -6.8226e-02, -1.9894e-02,\n",
      "          2.0004e-04, -1.1426e-02, -6.6670e-02, -1.5640e-01, -1.9572e-01,\n",
      "         -9.4480e-02, -9.8592e-02, -3.9975e-02, -4.0850e-03,  1.2016e-02,\n",
      "          6.1732e-02,  1.0335e-01,  1.3593e-01,  7.8866e-02,  1.1227e-01,\n",
      "          6.5775e-02,  6.4313e-02,  5.5494e-02,  4.2055e-03,  2.4744e-02,\n",
      "         -1.8264e-02, -7.3225e-02,  3.0189e-02, -1.3626e-01, -1.3769e-01,\n",
      "         -1.3261e-01, -1.7211e-02, -6.5026e-02, -2.4744e-02,  2.0464e-02,\n",
      "         -2.0672e-01, -3.2321e-01, -2.0486e-01, -8.1191e-02, -1.4633e-02,\n",
      "          4.2232e-02,  2.4507e-02, -2.0614e-03, -7.7360e-03, -4.0342e-03,\n",
      "         -5.4755e-03,  1.8181e-02,  3.3461e-02,  1.1480e-01,  1.2880e-02,\n",
      "          1.1370e-01,  2.1873e-01,  1.1588e-01,  4.0793e-02, -5.3127e-02,\n",
      "          6.3719e-03, -2.9765e-01, -1.9735e-01, -1.8109e-01, -1.4150e-02,\n",
      "         -3.1154e-03, -2.3854e-02, -2.4408e-02,  2.6516e-02, -2.6866e-01,\n",
      "         -3.1027e-01, -8.4385e-02, -1.7505e-01, -1.5179e-01, -2.1086e-01,\n",
      "         -1.0598e-01, -9.7155e-02, -1.3293e-01, -1.1638e-01, -8.2547e-02,\n",
      "          3.4645e-03, -5.1544e-02, -5.4290e-03, -5.9048e-02, -2.2211e-02,\n",
      "         -8.4992e-03, -2.5438e-01, -1.9468e-01, -7.9954e-02, -1.6186e-01,\n",
      "         -9.7765e-02, -1.1405e-01,  1.5415e-02, -3.0534e-02, -3.5700e-02,\n",
      "          3.1147e-02,  3.8875e-03, -1.6036e-01, -2.8432e-01, -3.0041e-01,\n",
      "         -2.3518e-01, -2.6886e-01, -3.1985e-01, -2.8868e-01, -1.5882e-01,\n",
      "         -1.6260e-01, -1.8721e-01, -2.4599e-01, -3.6674e-01, -2.6950e-01,\n",
      "         -1.5296e-01, -1.0933e-01, -2.7936e-01, -2.7776e-01, -2.2313e-02,\n",
      "          7.6713e-02,  9.0028e-02, -1.8787e-02, -2.6714e-02,  4.8593e-02,\n",
      "          4.8831e-03,  1.4979e-02,  2.5184e-02, -2.4393e-02, -2.2781e-02,\n",
      "         -3.6722e-02, -1.7625e-02, -3.2502e-02, -5.7145e-02, -1.0672e-01,\n",
      "         -1.2101e-01, -1.7082e-01, -1.0621e-01, -1.6551e-01, -2.0744e-01,\n",
      "         -1.3231e-01, -1.1533e-01, -4.6918e-02, -1.0766e-01, -9.2018e-02,\n",
      "         -1.1540e-01, -5.8026e-02, -2.8721e-02, -1.9985e-02, -4.7146e-03,\n",
      "          2.6579e-02,  8.6050e-03,  1.0013e-02,  2.0783e-03],\n",
      "        [-3.5640e-02,  1.0470e-02,  2.2180e-03, -1.7068e-02, -2.1624e-02,\n",
      "          2.7613e-02, -1.0281e-02,  7.6408e-03, -2.1682e-02,  3.3593e-02,\n",
      "         -2.7325e-02,  3.0555e-02,  4.7689e-03, -3.0884e-02, -9.0883e-03,\n",
      "         -5.8046e-04, -1.8793e-02,  2.0336e-03, -2.7517e-02, -2.0650e-02,\n",
      "         -1.5644e-02,  2.0786e-02,  1.5314e-02, -3.9968e-04,  3.4023e-02,\n",
      "         -2.3221e-03, -6.0547e-03,  2.6469e-02,  1.5085e-02,  1.5635e-02,\n",
      "          1.1448e-02, -4.3912e-03,  8.3652e-03, -6.6212e-04, -9.4619e-03,\n",
      "         -6.4793e-03, -1.1779e-03,  3.2712e-02,  5.8555e-03, -1.7614e-02,\n",
      "         -8.8953e-02, -1.1739e-02, -5.4208e-02, -1.1070e-01, -1.4002e-01,\n",
      "         -1.0446e-01, -5.6785e-02, -8.3053e-02, -1.5112e-01, -1.3230e-01,\n",
      "         -1.3905e-01, -7.6807e-02, -2.4455e-02, -1.9879e-02, -2.8719e-03,\n",
      "          2.6535e-02,  1.1247e-03, -1.4079e-02, -2.9131e-02,  8.7296e-04,\n",
      "         -2.5695e-02, -2.0271e-02,  1.5926e-02,  2.8909e-02,  1.9483e-02,\n",
      "         -3.9663e-02, -2.6387e-02, -7.1224e-02, -6.3536e-02, -5.3586e-02,\n",
      "         -8.0291e-02, -1.7483e-01, -1.6707e-01, -2.0653e-01, -1.2618e-01,\n",
      "         -1.5136e-01, -2.0951e-01, -9.7601e-02, -1.6613e-01, -1.2151e-01,\n",
      "          3.1384e-02,  2.9082e-02,  1.2877e-02, -2.9978e-02, -2.7563e-04,\n",
      "         -2.1659e-02, -1.5655e-03, -6.5767e-02, -2.5891e-03, -4.2351e-03,\n",
      "         -2.6276e-02, -3.4880e-02, -2.0917e-02, -5.1107e-02, -4.2846e-02,\n",
      "         -1.4734e-01, -2.6811e-01, -1.8780e-01, -2.4478e-01, -2.6414e-01,\n",
      "         -2.4696e-01, -1.3546e-01, -8.6486e-02, -1.6817e-01, -2.0184e-01,\n",
      "         -2.1438e-01, -1.3665e-01, -4.1032e-02, -7.8301e-03,  1.6723e-02,\n",
      "         -4.8880e-02, -1.9050e-02,  2.1921e-02,  3.1465e-02, -4.6864e-03,\n",
      "         -3.0380e-02,  1.5247e-02, -5.7884e-02,  5.1291e-03, -1.5689e-01,\n",
      "         -1.6594e-01, -1.3399e-01, -2.2837e-01, -2.9520e-01, -4.4410e-01,\n",
      "         -4.6207e-01, -4.7951e-01, -5.1835e-01, -5.3883e-01, -4.8877e-01,\n",
      "         -3.3450e-01, -3.8451e-01, -2.7433e-01, -1.8664e-01, -1.8095e-01,\n",
      "         -4.7599e-02, -8.3668e-02, -2.8135e-02, -1.6092e-02,  6.5047e-03,\n",
      "         -1.2943e-02,  1.6444e-02,  2.1694e-02, -4.2408e-02, -4.5392e-02,\n",
      "         -1.1427e-01, -2.5591e-01, -3.4737e-01, -2.5138e-01, -3.1291e-01,\n",
      "         -2.2199e-01, -5.6126e-02, -1.0804e-01, -6.4790e-02, -1.5794e-01,\n",
      "         -1.8067e-01, -3.3187e-01, -2.4439e-01, -2.3330e-01, -1.6875e-01,\n",
      "         -1.6175e-01, -2.1447e-01, -3.4027e-01, -4.2141e-01, -3.0080e-01,\n",
      "         -1.0254e-01, -2.7200e-02,  1.1302e-02,  1.8556e-02, -8.3686e-02,\n",
      "         -4.8372e-02, -4.1527e-02, -1.8564e-01, -3.6682e-01, -3.6932e-01,\n",
      "         -4.4860e-01, -2.5738e-01, -1.4791e-01, -1.2631e-01,  3.6333e-02,\n",
      "          7.5199e-02,  1.9867e-01,  1.8985e-01,  1.5800e-01,  1.7747e-01,\n",
      "          1.2979e-01,  7.2460e-02,  4.9775e-02,  4.7591e-02, -8.2570e-02,\n",
      "         -2.5418e-01, -3.7759e-01, -3.4417e-01, -1.4221e-01, -1.6881e-01,\n",
      "         -3.1960e-02,  9.6139e-03, -1.3377e-01, -1.4504e-01, -1.7820e-01,\n",
      "         -4.0200e-01, -3.9203e-01, -3.2494e-01, -2.1276e-01, -1.0953e-01,\n",
      "         -1.1594e-01, -2.1486e-02,  2.2214e-02,  8.4245e-02,  1.5575e-01,\n",
      "          2.4996e-01,  3.0770e-01,  2.6291e-01,  1.1200e-01,  7.6541e-02,\n",
      "         -1.7385e-02, -2.2617e-02, -2.2697e-04, -9.2947e-02, -1.8785e-01,\n",
      "         -3.4059e-01, -3.0629e-01, -1.9313e-01,  2.5297e-02, -1.0919e-01,\n",
      "         -1.0662e-01, -1.7520e-01, -2.7871e-01, -2.2576e-01, -2.2020e-01,\n",
      "         -1.0828e-01, -1.4957e-01, -8.9289e-02, -8.8231e-02, -5.2256e-02,\n",
      "         -7.9861e-02, -4.0974e-02,  4.8259e-02,  1.1347e-01,  1.8261e-01,\n",
      "          7.7848e-02, -1.2124e-02, -3.3473e-02, -9.7255e-02, -4.0564e-02,\n",
      "         -5.5227e-03, -9.2895e-02, -8.4855e-02, -2.4484e-01, -3.1877e-01,\n",
      "         -1.8835e-01, -2.8601e-02, -4.6716e-04, -4.5328e-02, -8.3740e-02,\n",
      "         -1.6297e-01, -2.6125e-01, -1.1097e-01, -2.2942e-02, -3.2878e-02,\n",
      "         -4.1463e-02, -6.1639e-02, -4.5973e-02, -1.8071e-02, -4.0103e-02,\n",
      "          4.8594e-03,  8.4248e-02,  2.1917e-02, -3.5664e-02, -9.5393e-02,\n",
      "         -5.3603e-02, -8.7914e-02, -6.2191e-02, -1.0286e-01, -1.1671e-01,\n",
      "         -1.7553e-01, -2.5000e-01, -2.7555e-01, -2.3392e-01, -1.0662e-01,\n",
      "          1.7071e-02, -6.1527e-02, -1.6143e-01, -1.7005e-01, -1.9255e-01,\n",
      "         -7.9232e-02, -2.2824e-03, -3.2901e-02,  4.3525e-02, -6.8776e-03,\n",
      "          4.7289e-02,  6.2723e-02, -1.0573e-02, -8.0812e-02, -1.5143e-02,\n",
      "         -7.8704e-02, -1.2630e-01, -3.7343e-02, -1.2004e-02, -3.2122e-02,\n",
      "         -7.1372e-03, -4.1573e-02,  5.9800e-03, -9.0171e-02, -2.1896e-01,\n",
      "         -1.0284e-01, -2.2149e-01, -1.1168e-01,  2.5671e-02, -9.4031e-02,\n",
      "         -1.5952e-01, -2.6878e-01, -4.4096e-02,  3.5508e-02,  5.3883e-02,\n",
      "          7.8436e-02,  1.2187e-01,  3.6945e-02,  5.4807e-02,  8.7258e-02,\n",
      "         -4.2568e-02, -6.7753e-02, -2.4659e-02,  6.7417e-03,  6.7212e-03,\n",
      "          5.0716e-02,  3.4998e-02,  9.3457e-02,  1.3790e-01,  9.0776e-02,\n",
      "          7.9160e-02, -5.3166e-02, -3.1243e-01, -2.9383e-01, -2.9460e-01,\n",
      "         -1.8412e-01, -7.0155e-02, -1.9100e-01, -1.7333e-01, -2.3147e-01,\n",
      "          1.8845e-02,  1.4848e-01,  1.0609e-01,  1.0405e-01,  7.8529e-02,\n",
      "          5.8472e-02,  8.1600e-02,  1.3751e-02, -3.3898e-02,  8.4571e-02,\n",
      "          1.2929e-01,  1.3014e-01,  5.9750e-02,  1.1879e-01,  1.2035e-01,\n",
      "          1.2189e-01,  1.2432e-01,  1.5768e-01,  1.7697e-01,  1.2061e-01,\n",
      "         -3.4924e-01, -4.0221e-01, -2.0573e-01, -9.0293e-02, -1.0409e-01,\n",
      "         -1.6241e-01, -2.8462e-01, -9.3049e-02,  7.3189e-02,  1.3975e-01,\n",
      "          9.4171e-02,  7.1037e-02,  7.5097e-02,  5.6378e-02,  7.7207e-02,\n",
      "          2.8376e-02, -4.0145e-03,  6.7937e-02,  1.3698e-01,  1.0981e-01,\n",
      "          6.9779e-02,  1.0581e-01,  8.9775e-02,  9.9614e-02,  6.8832e-02,\n",
      "          1.3234e-01,  3.1844e-03, -2.1990e-01, -4.3341e-01, -2.5451e-01,\n",
      "         -1.3128e-01, -7.1854e-02, -1.1967e-01, -4.3670e-02, -2.2317e-01,\n",
      "         -5.7073e-02,  4.1958e-02,  4.5395e-02,  6.0131e-02, -2.2267e-03,\n",
      "          4.1387e-03,  2.0563e-02,  4.7483e-02,  5.6369e-02,  5.8783e-02,\n",
      "          4.6717e-02,  6.7484e-02,  7.9765e-02,  1.0048e-01,  8.8117e-02,\n",
      "         -7.9170e-04, -1.8315e-03, -2.0160e-02, -4.5659e-03, -6.2554e-02,\n",
      "         -2.9184e-01, -4.3180e-01, -1.2009e-01, -6.5816e-02,  2.4245e-02,\n",
      "         -6.0078e-02,  2.3171e-03, -2.6272e-01, -1.4394e-01,  1.1033e-02,\n",
      "          2.7592e-02, -9.2040e-02,  3.7974e-03, -2.6289e-03, -2.6676e-02,\n",
      "          5.7297e-02,  6.0769e-02,  3.7285e-02, -1.7004e-02, -1.4644e-02,\n",
      "          3.7056e-02,  1.2056e-01,  8.8574e-02, -2.9397e-02, -5.5222e-02,\n",
      "         -6.2026e-02, -1.1396e-01, -1.4073e-01, -2.6165e-01, -3.4480e-01,\n",
      "         -2.0483e-01, -4.3797e-02, -5.5304e-02, -6.3149e-03, -1.8839e-02,\n",
      "         -1.7596e-01, -3.9806e-02, -8.7195e-03,  4.4843e-03, -1.0766e-01,\n",
      "         -1.4079e-02,  3.0513e-02,  1.7306e-02,  1.1362e-01,  1.1768e-01,\n",
      "          1.7048e-02, -4.3133e-02, -7.1805e-02, -1.3949e-02,  4.6962e-02,\n",
      "         -1.9879e-02, -3.0458e-02, -5.0692e-02, -7.5754e-02, -1.4526e-01,\n",
      "         -7.1361e-02, -2.4513e-01, -4.0900e-01, -3.6842e-01, -1.0430e-01,\n",
      "         -9.4511e-02,  2.8305e-02,  8.2938e-03, -1.6770e-01, -8.1584e-02,\n",
      "         -7.7575e-02, -1.0309e-01, -3.3945e-02,  1.7682e-02,  1.5664e-02,\n",
      "          3.5985e-02,  7.6967e-02,  7.5106e-02,  2.2368e-02, -1.6940e-02,\n",
      "         -4.8761e-02,  6.7308e-02, -3.7566e-03, -1.8639e-02, -1.0480e-02,\n",
      "         -9.6963e-03, -4.1214e-02, -6.4049e-02, -1.1497e-01, -2.8112e-01,\n",
      "         -4.2441e-01, -8.1736e-02, -1.5316e-01, -1.4508e-01,  1.2601e-01,\n",
      "         -5.1660e-02, -1.6569e-01, -2.1684e-01, -1.9009e-01, -1.9619e-01,\n",
      "         -2.2574e-02, -2.2108e-02,  2.9553e-02,  8.0228e-02,  9.6276e-02,\n",
      "          8.2943e-02,  1.8226e-04, -2.6333e-02,  2.7592e-02, -1.4521e-02,\n",
      "         -1.4834e-02, -1.0649e-02, -2.9345e-02, -1.7470e-02,  1.6267e-02,\n",
      "         -1.0635e-01, -1.6802e-01, -2.7904e-01, -4.3987e-01, -1.7879e-01,\n",
      "         -2.1798e-01, -1.1879e-01, -3.0967e-02, -1.9890e-01, -1.7414e-01,\n",
      "         -1.8650e-01, -3.1717e-01, -1.9710e-01, -1.0725e-01, -4.3863e-02,\n",
      "         -6.2995e-02, -1.2688e-01, -4.6840e-02,  1.8835e-02, -4.6562e-02,\n",
      "         -5.5608e-02, -5.3031e-02, -8.2358e-02, -4.1150e-02,  4.2817e-03,\n",
      "         -4.4050e-02, -5.1630e-02, -3.8729e-02, -1.3048e-01, -6.9548e-02,\n",
      "         -1.7517e-01, -2.3877e-01, -8.1616e-02, -1.7695e-01, -3.5989e-02,\n",
      "         -2.2327e-02, -1.2093e-01, -1.1569e-01, -1.3852e-01, -1.3992e-01,\n",
      "         -1.0385e-01, -1.7628e-01, -2.3570e-01, -2.7530e-01, -2.8694e-01,\n",
      "         -2.5464e-01, -1.6170e-01, -1.2104e-01, -4.0749e-02, -7.7701e-02,\n",
      "         -7.4685e-02, -5.5243e-02, -4.7628e-02, -6.1311e-02, -8.7468e-02,\n",
      "         -7.0268e-02, -1.0691e-01, -6.7757e-02, -9.3939e-02, -3.6353e-02,\n",
      "          9.0646e-02, -1.9110e-01,  3.3470e-02,  1.0580e-02, -4.9576e-02,\n",
      "         -7.9441e-03, -1.6389e-01, -8.4805e-02, -1.5061e-01, -2.9329e-01,\n",
      "         -3.3107e-01, -2.4245e-01, -1.2237e-01, -2.2075e-01, -1.8050e-01,\n",
      "         -1.0255e-01, -6.6155e-02, -7.9571e-02, -8.5118e-02, -9.2780e-02,\n",
      "         -1.2752e-01, -9.1664e-02, -9.2615e-02, -3.0783e-02, -3.9067e-02,\n",
      "         -3.9697e-02,  6.9480e-02,  8.4374e-02,  1.2201e-01, -1.3693e-01,\n",
      "         -3.4995e-03, -2.3072e-02, -2.6996e-03, -1.3400e-01, -1.7902e-01,\n",
      "         -4.6089e-02, -2.5609e-01, -2.7169e-01, -1.9067e-01, -2.6513e-02,\n",
      "         -5.4362e-02, -9.4550e-02, -8.6864e-02, -5.5562e-02, -9.4804e-02,\n",
      "         -5.4952e-02, -1.3248e-01, -1.2240e-01, -1.2339e-01, -6.8760e-02,\n",
      "         -1.4177e-01, -8.7277e-02, -4.6124e-02,  8.1920e-02,  1.0877e-01,\n",
      "          7.5050e-02,  8.5210e-02, -1.5029e-01,  2.3513e-02, -2.2933e-02,\n",
      "          3.2013e-02, -8.9346e-02, -7.7845e-02, -1.9061e-01, -1.5937e-01,\n",
      "         -1.3825e-01, -7.3677e-02,  1.4453e-02, -6.1302e-02, -2.8052e-02,\n",
      "         -6.9956e-02, -7.5874e-02, -1.1574e-01, -9.9368e-02, -1.2425e-01,\n",
      "         -1.1909e-01, -1.0686e-01, -7.3139e-02, -5.3408e-02, -4.1869e-02,\n",
      "         -1.4967e-02,  1.9680e-01,  2.5629e-01,  1.3307e-01,  2.7675e-02,\n",
      "         -1.1160e-01,  1.7500e-02,  2.9094e-02, -3.0566e-03, -3.5986e-02,\n",
      "         -1.8419e-01, -1.0767e-01,  5.9909e-02,  2.4394e-02,  2.8180e-02,\n",
      "         -1.9894e-02, -3.8614e-02, -4.5372e-02, -7.7457e-02, -8.4171e-02,\n",
      "         -1.2181e-01, -1.0779e-01, -8.5359e-02, -4.4170e-02,  2.3582e-02,\n",
      "         -5.1178e-03,  4.7375e-02,  1.5572e-01,  2.0973e-01,  2.1655e-01,\n",
      "          3.3485e-01,  4.8577e-02, -2.9234e-02, -9.4235e-02,  1.5055e-02,\n",
      "         -1.7265e-02,  7.2518e-03,  1.1946e-01, -1.4565e-01, -2.5935e-02,\n",
      "          1.9712e-01,  8.7631e-02, -2.8591e-02, -3.7887e-02, -3.3616e-02,\n",
      "          1.5147e-02, -3.8774e-02,  6.6530e-02,  4.8931e-02,  3.6665e-02,\n",
      "          5.7746e-02,  1.3019e-01,  2.2277e-01,  2.6011e-01,  2.5918e-01,\n",
      "          4.0761e-01,  4.1722e-01,  2.7195e-01,  2.1509e-01,  8.2830e-04,\n",
      "         -6.7360e-03, -9.2426e-02, -1.2576e-02,  4.9031e-03, -1.8895e-02,\n",
      "          2.1719e-03,  1.0173e-01,  2.6285e-01,  2.0417e-01,  1.5962e-01,\n",
      "          1.5673e-01,  1.5419e-01,  1.7640e-01,  1.2772e-01,  1.0161e-01,\n",
      "          2.1387e-01,  1.5381e-01,  2.6680e-01,  1.3923e-01,  1.6720e-01,\n",
      "          1.4583e-01,  2.7974e-01,  2.3551e-01,  3.4154e-01,  2.2680e-01,\n",
      "          1.1452e-01,  1.2336e-01,  3.6882e-02, -1.2406e-02,  2.6124e-02,\n",
      "          2.2042e-02, -2.9165e-02,  2.4581e-02, -7.8615e-03, -1.6352e-02,\n",
      "         -5.5299e-04, -1.2466e-01, -4.2446e-02,  4.8969e-02, -4.9473e-03,\n",
      "          2.1037e-02,  9.3977e-02,  5.0290e-02,  5.4531e-02, -2.2692e-01,\n",
      "          1.3590e-01,  6.1325e-03, -7.2220e-02, -1.9120e-01, -2.5419e-02,\n",
      "         -6.9757e-02, -4.0302e-03, -8.4211e-02, -3.1448e-02,  1.6941e-02,\n",
      "         -2.7635e-02, -5.4469e-03,  2.0787e-02, -3.6228e-03]], device='cuda:0',\n",
      "       requires_grad=True)\n",
      "SNN.linear_1.weight torch.Size([10, 784])\n"
     ]
    }
   ],
   "source": [
    "# 查看网络参数\n",
    "print(net)\n",
    "net.named_parameters()\n",
    "for name, param in net.named_parameters(prefix=\"SNN\"):\n",
    "    print(name, param)\n",
    "    print(name, param.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 清除显存\n",
    "torch.cuda.empty_cache()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "SpikingJelly_zzy",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
